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We study the timelike asymptotics for global solutions to a scalar quasilinear wave equation satisfying the weak null condition. Given a global solution $u$ to the scalar wave equation with sufficiently small $C_c^\infty$ initial data, we derive an asymptotic formula for this global solution inside the light cone (i.e. for $|x|

The task that requires an agent to navigate to a given object through only visual observation is called visual object navigation (VON). The main bottlenecks of VON are strategies exploration and prior knowledge exploitation. Traditional strategies exploration ignores the differences of searching and navigating stages, using the same reward in two stages, which reduces navigation performance and training efficiency. Our study enables the agent to explore larger area in searching stage and seek the optimal path in navigating stage, improving the success rate of navigation. Traditional prior knowledge exploitation focused on learning and utilizing object association, which ignored the depth and obstacle information in the environment. This paper uses the RGB and depth information of the training scene to pretrain the feature extractor, which improves navigation efficiency. The obstacle information is memorized by the agent during the navigation, reducing the probability of collision and deadlock. Depth, obstacle and other prior knowledge are concatenated and input into the policy network, and navigation actions are output under the training of two-stage rewards. We evaluated our method on AI2-Thor and RoboTHOR and demonstrated that it significantly outperforms state-of-the-art (SOTA) methods on success rate and navigation efficiency.

The high resource consumption of large-scale models discourages resource-constrained users from developing their customized transformers. To this end, this paper considers a federated framework named Fed-Grow for multiple participants to cooperatively scale a transformer from their pre-trained small models. Under the Fed-Grow, a Dual-LiGO (Dual Linear Growth Operator) architecture is designed to help participants expand their pre-trained small models to a transformer. In Dual-LiGO, the Local-LiGO part is used to address the heterogeneity problem caused by the various pre-trained models, and the Global-LiGO part is shared to exchange the implicit knowledge from the pre-trained models, local data, and training process of participants. Instead of model sharing, only sharing the Global-LiGO strengthens the privacy of our approach. Compared with several state-of-the-art methods in simulation, our approach has higher accuracy, better precision, and lower resource consumption on computations and communications. To the best of our knowledge, most of the previous model-scaling works are centralized, and our work is the first one that cooperatively grows a transformer from multiple pre-trained heterogeneous models with the user privacy protected in terms of local data and models. We hope that our approach can extend the transformers to the broadly distributed scenarios and encourage more resource-constrained users to enjoy the bonus taken by the large-scale transformers.

The paper studies a fundamental federated learning (FL) problem involving multiple clients with heterogeneous constrained resources. Compared with the numerous training parameters, the computing and communication resources of clients are insufficient for fast local training and real-time knowledge sharing. Besides, training on clients with heterogeneous resources may result in the straggler problem. To address these issues, we propose Fed-RAA: a Resource-Adaptive Asynchronous Federated learning algorithm. Different from vanilla FL methods, where all parameters are trained by each participating client regardless of resource diversity, Fed-RAA adaptively allocates fragments of the global model to clients based on their computing and communication capabilities. Each client then individually trains its assigned model fragment and asynchronously uploads the updated result. Theoretical analysis confirms the convergence of our approach. Additionally, we design an online greedy-based algorithm for fragment allocation in Fed-RAA, achieving fairness comparable to an offline strategy. We present numerical results on MNIST, CIFAR-10, and CIFAR-100, along with necessary comparisons and ablation studies, demonstrating the advantages of our work. To the best of our knowledge, this paper represents the first resource-adaptive asynchronous method for fragment-based FL with guaranteed theoretical convergence.

The safety of decentralized reinforcement learning (RL) is a challenging problem since malicious agents can share their poisoned policies with benign agents. The paper investigates a cooperative backdoor attack in a decentralized reinforcement learning scenario. Differing from the existing methods that hide a whole backdoor attack behind their shared policies, our method decomposes the backdoor behavior into multiple components according to the state space of RL. Each malicious agent hides one component in its policy and shares its policy with the benign agents. When a benign agent learns all the poisoned policies, the backdoor attack is assembled in its policy. The theoretical proof is given to show that our cooperative method can successfully inject the backdoor into the RL policies of benign agents. Compared with the existing backdoor attacks, our cooperative method is more covert since the policy from each attacker only contains a component of the backdoor attack and is harder to detect. Extensive simulations are conducted based on Atari environments to demonstrate the efficiency and covertness of our method. To the best of our knowledge, this is the first paper presenting a provable cooperative backdoor attack in decentralized reinforcement learning.

Zero-shot object navigation (ZSON) addresses situation where an agent navigates to an unseen object that does not present in the training set. Previous works mainly train agent using seen objects with known labels, and ignore the seen objects without labels. In this paper, we introduce seen objects without labels, herein termed as ``unknown objects'', into training procedure to enrich the agent's knowledge base with distinguishable but previously overlooked information. Furthermore, we propose the label-wise meta-correlation module (LWMCM) to harness relationships among objects with and without labels, and obtain enhanced objects information. Specially, we propose target feature generator (TFG) to generate the features representation of the unlabeled target objects. Subsequently, the unlabeled object identifier (UOI) module assesses whether the unlabeled target object appears in the current observation frame captured by the camera and produces an adapted target features representation specific to the observed context. In meta contrastive feature modifier (MCFM), the target features is modified via approaching the features of objects within the observation frame while distancing itself from features of unobserved objects. Finally, the meta object-graph learner (MOGL) module is utilized to calculate the relationships among objects based on the features. Experiments conducted on AI2THOR and RoboTHOR platforms demonstrate the effectiveness of our proposed method.

In light of recent work of the third author, we revisit a classic example given by Fritz John of a semi-linear wave equation which exhibits finite in time blow up for all compactly supported data. We present the construction of future global solutions from asymptotic data given in arXiv:2204.12870(2022) for this specific example, and clarify the relation of this result of Yu to John's theorem. Furthermore we present a novel blow up result for finite energy solutions satisfying a sign condition due to the first author, and invoke this result to show that the constructed backwards in time solutions blow up in the past., Comment: Contributed article to MATRIX Annals, for the MATRIX workshop "Hyperbolic PDEs and Nonlinear Evolution Problems", held in Creswick, Australia, from September 17 -- 30, 2023

Vision-and-Language Navigation (VLN) is a challenging task where an agent is required to navigate to a natural language described location via vision observations. The navigation abilities of the agent can be enhanced by the relations between objects, which are usually learned using internal objects or external datasets. The relationships between internal objects are modeled employing graph convolutional network (GCN) in traditional studies. However, GCN tends to be shallow, limiting its modeling ability. To address this issue, we utilize a cross attention mechanism to learn the connections between objects over a trajectory, which takes temporal continuity into account, termed as Temporal Object Relations (TOR). The external datasets have a gap with the navigation environment, leading to inaccurate modeling of relations. To avoid this problem, we construct object connections based on observations from all viewpoints in the navigational environment, which ensures complete spatial coverage and eliminates the gap, called Spatial Object Relations (SOR). Additionally, we observe that agents may repeatedly visit the same location during navigation, significantly hindering their performance. For resolving this matter, we introduce the Turning Back Penalty (TBP) loss function, which penalizes the agent's repetitive visiting behavior, substantially reducing the navigational distance. Experimental results on the REVERIE, SOON, and R2R datasets demonstrate the effectiveness of the proposed method.

Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems by consolidating storage capacities from independent providers and coordinating to provide decentralized storage and retrieval services. However, current DSNs face several challenges associated with data privacy and efficiency of the proof systems. To address these issues, we propose FileDES (\uline{D}ecentralized \uline{E}ncrypted \uline{S}torage), which incorporates three essential elements: privacy preservation, scalable storage proof, and batch verification. FileDES provides encrypted data storage while maintaining data availability, with a scalable Proof of Encrypted Storage (PoES) algorithm that is resilient to Sybil and Generation attacks. Additionally, we introduce a rollup-based batch verification approach to simultaneously verify multiple files using publicly verifiable succinct proofs. We conducted a comparative evaluation on FileDES, Filecoin, Storj and Sia under various conditions, including a WAN composed of up to 120 geographically dispersed nodes. Our protocol outperforms the others in terms of proof generation/verification efficiency, storage costs, and scalability., Comment: 10 pages, 8 figures, 1 table. Accepted by 2024 IEEE INFOCOM

With the rapid development of blockchain and its applications, the amount of data stored on decentralized storage networks (DSNs) has grown exponentially. DSNs bring together affordable storage resources from around the world to provide robust, decentralized storage services for tens of thousands of decentralized applications (dApps). However, existing DSNs do not offer verifiability when implementing erasure coding for redundant storage, making them vulnerable to Byzantine encoders. Additionally, there is a lack of Byzantine fault-tolerant consensus for optimal resilience in DSNs. This paper introduces BFT-DSN, a Byzantine fault-tolerant decentralized storage network designed to address these challenges. BFT-DSN combines storage-weighted BFT consensus with erasure coding and incorporates homomorphic fingerprints and weighted threshold signatures for decentralized verification. The implementation of BFT-DSN demonstrates its comparable performance in terms of storage cost and latency as well as superior performance in Byzantine resilience when compared to existing industrial decentralized storage networks., Comment: 11 pages, 8 figures

We study federated unlearning, a novel problem to eliminate the impact of specific clients or data points on the global model learned via federated learning (FL). This problem is driven by the right to be forgotten and the privacy challenges in FL. We introduce a new framework for exact federated unlearning that meets two essential criteria: \textit{communication efficiency} and \textit{exact unlearning provability}. To our knowledge, this is the first work to tackle both aspects coherently. We start by giving a rigorous definition of \textit{exact} federated unlearning, which guarantees that the unlearned model is statistically indistinguishable from the one trained without the deleted data. We then pinpoint the key property that enables fast exact federated unlearning: total variation (TV) stability, which measures the sensitivity of the model parameters to slight changes in the dataset. Leveraging this insight, we develop a TV-stable FL algorithm called \texttt{FATS}, which modifies the classical \texttt{\underline{F}ed\underline{A}vg} algorithm for \underline{T}V \underline{S}tability and employs local SGD with periodic averaging to lower the communication round. We also design efficient unlearning algorithms for \texttt{FATS} under two settings: client-level and sample-level unlearning. We provide theoretical guarantees for our learning and unlearning algorithms, proving that they achieve exact federated unlearning with reasonable convergence rates for both the original and unlearned models. We empirically validate our framework on 6 benchmark datasets, and show its superiority over state-of-the-art methods in terms of accuracy, communication cost, computation cost, and unlearning efficacy.

Value function factorization has achieved great success in multi-agent reinforcement learning by optimizing joint action-value functions through the maximization of factorized per-agent utilities. To ensure Individual-Global-Maximum property, existing works often focus on value factorization using monotonic functions, which are known to result in restricted representation expressiveness. In this paper, we analyze the limitations of monotonic factorization and present ConcaveQ, a novel non-monotonic value function factorization approach that goes beyond monotonic mixing functions and employs neural network representations of concave mixing functions. Leveraging the concave property in factorization, an iterative action selection scheme is developed to obtain optimal joint actions during training. It is used to update agents' local policy networks, enabling fully decentralized execution. The effectiveness of the proposed ConcaveQ is validated across scenarios involving multi-agent predator-prey environment and StarCraft II micromanagement tasks. Empirical results exhibit significant improvement of ConcaveQ over state-of-the-art multi-agent reinforcement learning approaches., Comment: Accepted at AAAI 2024

A metaverse breaks the boundaries of time and space between people, realizing a more realistic virtual experience, improving work efficiency, and creating a new business model. Blockchain, as one of the key supporting technologies for a metaverse design, provides a trusted interactive environment. However, the rich and varied scenes of a metaverse have led to excessive consumption of on-chain resources, raising the threshold for ordinary users to join, thereby losing the human-centered design. Therefore, we propose an adaptive and modular blockchain-enabled architecture for a decentralized metaverse to address these issues. The solution includes an adaptive consensus/ledger protocol based on a modular blockchain, which can effectively adapt to the ever-changing scenarios of the metaverse, reduce resource consumption, and provide a secure and reliable interactive environment. In addition, we propose the concept of Non-Fungible Resource (NFR) to virtualize idle resources. Users can establish a temporary trusted environment and rent others' NFR to meet their computing needs. Finally, we simulate and test our solution based on XuperChain, and the experimental results prove the feasibility of our design., Comment: 11 pages, 11 figures, journal

Distributed stochastic optimization methods based on Newton's method offer significant advantages over first-order methods by leveraging curvature information for improved performance. However, the practical applicability of Newton's method is hindered in large-scale and heterogeneous learning environments due to challenges such as high computation and communication costs associated with the Hessian matrix, sub-model diversity, staleness in training, and data heterogeneity. To address these challenges, this paper introduces a novel and efficient algorithm called RANL, which overcomes the limitations of Newton's method by employing a simple Hessian initialization and adaptive assignments of training regions. The algorithm demonstrates impressive convergence properties, which are rigorously analyzed under standard assumptions in stochastic optimization. The theoretical analysis establishes that RANL achieves a linear convergence rate while effectively adapting to available resources and maintaining high efficiency. Unlike traditional first-order methods, RANL exhibits remarkable independence from the condition number of the problem and eliminates the need for complex parameter tuning. These advantages make RANL a promising approach for distributed stochastic optimization in practical scenarios.

We study the asymptotic stability of the sine-Gordon kinks under small perturbations in weighted Sobolev norms. Our main tool is the B\"acklund transform which reduces the study of the asymptotic stability of the kinks to the study of the asymptotic decay of solutions near zero. Our results consist of two parts. First, we prove an asymptotic stability result similar to the local results in arXiv:2003.09358 and arXiv:2009.04260. Our assumptions are the same as those in the local result in arXiv:2009.04260. In its proof, we apply a result obtained by the inverse scattering method on the local decay of the solutions with sufficiently small and localized initial data. Moreover, we derive an asymptotic formula for the perturbations, i.e. the difference between solutions and kinks. This result is similar to that in arXiv:2106.09605 and the full asymptotic stability result in arXiv:2009.04260. In its proof, we apply a result obtained by the method of testing by wave packets on the pointwise decay of the solutions with small and localized data., Comment: 63 pages. Major revision. The main results have been improved. Many thanks to the anonymous reviewers

Recent years have witnessed the fast penetration of Virtual Reality (VR) and Augmented Reality (AR) systems into our daily life, the security and privacy issues of the VR/AR applications have been attracting considerable attention. Most VR/AR systems adopt head-mounted devices (i.e., smart headsets) to interact with users and the devices usually store the users' private data. Hence, authentication schemes are desired for the head-mounted devices. Traditional knowledge-based authentication schemes for general personal devices have been proved vulnerable to shoulder-surfing attacks, especially considering the headsets may block the sight of the users. Although the robustness of the knowledge-based authentication can be improved by designing complicated secret codes in virtual space, this approach induces a compromise of usability. Another choice is to leverage the users' biometrics; however, it either relies on highly advanced equipments which may not always be available in commercial headsets or introduce heavy cognitive load to users. In this paper, we propose a vibration-based authentication scheme, VibHead, for smart headsets. Since the propagation of vibration signals through human heads presents unique patterns for different individuals, VibHead employs a CNN-based model to classify registered legitimate users based the features extracted from the vibration signals. We also design a two-step authentication scheme where the above user classifiers are utilized to distinguish the legitimate user from illegitimate ones. We implement VibHead on a Microsoft HoloLens equipped with a linear motor and an IMU sensor which are commonly used in off-the-shelf personal smart devices. According to the results of our extensive experiments, with short vibration signals ($\leq 1s$), VibHead has an outstanding authentication accuracy; both FAR and FRR are around 5%.

Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration provides a big picture on the endeavors made by blockchain professionals over the years to enhance the blockchain performance while the micro-level investigation details the blockchain building blocks for deep technology comprehension. More specifically, this article introduces a general modular blockchain analytic framework that decomposes a blockchain system into interacting modules and then examines the major modules to cover the essential blockchain components of network, consensus, and distributed ledger at the micro-level. The framework as well as the modular analysis jointly build a foundation for designing scalable, flexible, and application-adaptive blockchains that can meet diverse requirements. Additionally, this article explores popular technologies that can be integrated with blockchain to expand functionality and highlights major challenges. Such a study provides critical insights to overcome the obstacles in designing novel blockchain systems and facilitates the further development of blockchain as a digital infrastructure to service new applications., Comment: 40 pages, 8 figures

Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algorithms may fail when dealing with real-world irregular data that behaves in a heavy-tailed manner. To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously. Specifically, we first present a Byzantine-resilient distributed gradient descent algorithm that can handle the heavy-tailed data and meanwhile converge under the standard assumptions. To reduce the communication overhead, we further propose another algorithm that incorporates gradient compression techniques to save communication costs during the learning process. Theoretical analysis shows that our algorithms achieve order-optimal statistical error rate in presence of Byzantine devices. Finally, we conduct extensive experiments on both synthetic and real-world datasets to verify the efficacy of our algorithms.

With the continuous improvement of information infrastructures, academia and industry have been constantly exploring new computing paradigms to fully exploit computing powers. In this paper, we propose Meta Computing, a new computing paradigm that aims to utilize all available computing resources hooked on the Internet, provide efficient, fault-tolerant, and personalized services with strong security and privacy guarantee, and virtualize the Internet as a giant computer, that is, ``Network-as-a-Computer, NaaC'', or ``Meta Computer'' for short, for any task or any person on-demand., Comment: 10 papes, 4 figures

Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers and ensure strong security. However, existing DSNs face two major challenges. First, deduplication can only be achieved at the directory-level. Missing file-level deduplication leads to unavoidable extra storage and bandwidth cost. Second, current DSNs realize file indexing by storing extra metadata while blockchain ledgers are not fully exploited. To overcome these problems, we propose FileDAG, a DSN built on DAG-based blockchain to support file-level deduplication in storing multi-versioned files. When updating files, we adopt an increment generation method to calculate and store only the increments instead of the entire updated files. Besides, we introduce a two-layer DAG-based blockchain ledger, by which FileDAG can provide flexible and storage-saving file indexing by directly using the blockchain database without incurring extra storage overhead. We implement FileDAG and evaluate its performance with extensive experiments. The results demonstrate that FileDAG outperforms the state-of-the-art industrial DSNs considering storage cost and latency.

Cross-chain technology facilitates the interoperability among isolated blockchains on which users can freely communicate and transfer values. Existing cross-chain protocols suffer from the scalability problem when processing on-chain transactions. Off-chain channels, as a promising blockchain scaling technique, can enable micro-payment transactions without involving on-chain transaction settlement. However, existing channel schemes can only be applied to operations within a single blockchain, failing to support cross-chain services. Therefore in this paper, we propose Cross-Channel, the first off-chain channel to support cross-chain services. We introduce a novel hierarchical channel structure, a new hierarchical settlement protocol, and a smart general fair exchange protocol, to ensure scalability, fairness, and atomicity of cross-chain interactions. Besides, Cross-Channel provides strong security and practicality by avoiding high latency in asynchronous networks. Through a 50-instance deployment of Cross-Channel on AliCloud, we demonstrate that Cross-Channel is well-suited for processing cross-chain transactions in high-frequency and large-scale, and brings a significantly enhanced throughput with a small amount of gas and delay overhead.

Metaverse has rekindled human beings' desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee., Comment: 7 pages, 4 figures

Payment channel network (PCN) is a layer-two scaling solution that enables fast off-chain transactions but does not involve on-chain transaction settlement. PCNs raise new privacy issues including balance secrecy, relationship anonymity and payment privacy. Moreover, protecting privacy causes low transaction success rates. To address this dilemma, we propose zk-PCN, a privacy-preserving payment channel network using zk-SNARKs. We prevent from exposing true balances by setting up \textit{public balances} instead. Using public balances, zk-PCN can guarantee high transaction success rates and protect PCN privacy with zero-knowledge proofs. Additionally, zk-PCN is compatible with the existing routing algorithms of PCNs. To support such compatibility, we propose zk-IPCN to improve zk-PCN with a novel proof generation (RPG) algorithm. zk-IPCN reduces the overheads of storing channel information and lowers the frequency of generating zero-knowledge proofs. Finally, extensive simulations demonstrate the effectiveness and efficiency of zk-PCN in various settings., Comment: 8 pages, 9 figures

In this paper, we seek to construct nontrivial global solutions to some quasilinear wave equations in three space dimensions. We first present a conditional result on the construction of nontrivial global solutions to a general system of quasilinear wave equations. Assuming that a global solution to the geometric reduced system exists and satisfies several well-chosen pointwise estimates, we find a matching exact global solution to the original wave equations. Such a conditional result is then applied to two types of equations which are of great interest. One is John's counterexamples $\Box u=u_t^2$ or $\Box u=u_t u_{tt}$, and the other is the 3D compressible Euler equations with no vorticity. We explicitly construct global solutions to the corresponding geometric reduced systems and show that these global solutions satisfy the required pointwise bounds. As a result, there exists a large family of nontrivial global solutions to these two types of equations., Comment: 134 pages. Major revision. Many thanks to the anonymous reviewers

We consider a K-armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and communication bandwidth. The goal is to let each of the agents eventually learn the best arm. It is assumed in these studies that the communication graph should be complete or well-structured, whereas such an assumption is not always valid in practice. Furthermore, limited memorization and communication bandwidth also restrict the collaborations of the agents, since the agents memorize and communicate very few experiences. Additionally, an agent may be corrupted to share falsified experiences to its peers, while the resource limit in terms of memorization and communication may considerably restrict the reliability of the learning process. To address the above issues, we propose a three-staged collaborative learning algorithm. In each step, the agents share their latest experiences with each other through light-weight random walks in a general communication graph, and then make decisions on which arms to pull according to the recommendations received from their peers. The agents finally update their adoptions (i.e., preferences to the arms) based on the reward obtained by pulling the arms. Our theoretical analysis shows that, when there are a sufficient number of agents participating in the collaborative learning process, all the agents eventually learn the best arm with high probability, even with limited memorizing capabilities and light-weight communications. We also reveal in our theoretical analysis the upper bound on the number of corrupted agents our algorithm can tolerate. The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.

Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which can be further serialized to form service function chains (SFCs), providing customized networking services. To ensure the availability of SFCs, it turns out to be effective to place redundant SFC backups at the edge for quickly recovering from any failures. The existing research largely overlooks the influences of SFC popularity, backup completeness and failure rate on the optimal deployment of SFC backups on edge servers. In this paper, we comprehensively consider from the perspectives of both the end users and edge system to backup SFCs for providing popular services with the lowest latency. To overcome the challenges resulted from unknown SFC popularity and failure rate, as well as the known system parameter constraints, we take advantage of the online bandit learning technique to cope with the uncertainty issue. Combining the Prim-inspired method with the greedy strategy, we propose a Real-Time Selection and Deployment(RTSD) algorithm. Extensive simulation experiments are conducted to demonstrate the superiority of our proposed algorithms., Comment: 13 pages,11 figures

Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies have shown the feasibility of preserving privacy, enhancing training performance or introducing Byzantine resilience, but none of them simultaneously considers all of them. Therefore we face the following problem: \textit{how can we efficiently coordinate the decentralized learning process while simultaneously maintaining learning security and data privacy?} To address this issue, in this paper we propose SPDL, a blockchain-secured and privacy-preserving decentralized learning scheme. SPDL integrates blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR), and differential privacy seamlessly into one system, ensuring efficient machine learning while maintaining data privacy, Byzantine fault tolerance, transparency, and traceability. To validate our scheme, we provide rigorous analysis on convergence and regret in the presence of Byzantine nodes. We also build a SPDL prototype and conduct extensive experiments to demonstrate that SPDL is effective and efficient with strong security and privacy guarantees., Comment: 11 pages, 7 figures

In this paper, we prove a uniqueness theorem for a system of semilinear wave equations satisfying the null condition in $\mathbb{R}^{1+3}$. Suppose that two global solutions with $C_c^\infty$ initial data have equal initial data outside a ball and equal radiation fields outside a light cone. We show that these two solutions are equal either outside a hyperboloid or everywhere in the spacetime, depending on the sizes of the ball and the light cone., Comment: 43 pages, 1 figure. Revised based on a referee report

This paper studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a Decentralized-Wireless-Federated-Learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies $(\epsilon,\delta)$-differential privacy and the privacy budget per worker scales as $\mathcal{O}(\frac{1}{\sqrt{N}})$, in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of $\mathcal{O}(\sqrt{\frac{1}{TN}})$ as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.

Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware variants can be generated by reusing codes, malware attacks can be easily launched and thus become common in recent years, incurring huge losses in businesses, governments, financial institutes, health providers, etc. To defeat these attacks, malware classification is employed, which plays an essential role in anti-virus products. However, existing works that employ either static analysis or dynamic analysis have major weaknesses in complicated reverse engineering and time-consuming tasks. In this paper, we propose a visualized malware classification framework called VisMal, which provides highly efficient categorization with acceptable accuracy. VisMal converts malware samples into images and then applies a contrast-limited adaptive histogram equalization algorithm to enhance the similarity between malware image regions in the same family. We provided a proof-of-concept implementation and carried out an extensive evaluation to verify the performance of our framework. The evaluation results indicate that VisMal can classify a malware sample within 4.0ms and have an average accuracy of 96.0%. Moreover, VisMal provides security engineers with a simple visualization approach to further validate its performance.

Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive number of workers to perform a given sensing task under a limited budget. Nevertheless, due to the proliferation of smart devices equipped with various sensors, it is very difficult to profile the workers in terms of sensing ability. Although the uncertainties of the workers can be addressed by standard Combinatorial Multi-Armed Bandit (CMAB) framework through a trade-off between exploration and exploitation, we do not have sufficient allowance to directly explore and exploit the workers under the limited budget. Furthermore, since the sensor devices usually have quite limited resources, the workers may have bounded capabilities to perform the sensing task for only few times, which further restricts our opportunities to learn the uncertainty. To address the above issues, we propose a Context-Aware Worker Selection (CAWS) algorithm in this paper. By leveraging the correlation between the context information of the workers and their sensing abilities, CAWS aims at maximizing the expected total sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers is massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments.

Blockchain creates a secure environment on top of strict cryptographic assumptions and rigorous security proofs. It permits on-chain interactions to achieve trustworthy properties such as traceability, transparency, and accountability. However, current blockchain trustworthiness is only confined to on-chain, creating a "trust gap" to the physical, off-chain environment. This is due to the lack of a scheme that can truthfully reflect the physical world in a real-time and consistent manner. Such an absence hinders further real-world blockchain applications, especially for security-sensitive ones. In this paper, we propose a scheme to extend blockchain trust from on-chain to off-chain, and take trustworthy vaccine transportation as an example. Our scheme consists of 1) a Trusted Execution Environment (TEE)-enabled trusted environment monitoring system built with the Arm Cortex-M33 microcontroller that continuously senses the inside of a vaccine box through trusted sensors and generates anti-forgery data; and 2) a consistency protocol to upload the environment status data from the TEE system to blockchain in a truthful, real-time consistent, continuous and fault-tolerant fashion. Our security analysis indicates that no adversary can tamper with the vaccine in any way without being captured. We carry out an experiment to record the internal status of a vaccine shipping box during transportation, and the results indicate that the proposed system incurs an average latency of 84 ms in local sensing and processing followed by an average latency of 130 ms to have the sensed data transmitted to and available in the blockchain.

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain -- CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain., Comment: 12 pages, 8 figures, journal paper

In this paper, we prove the first asymptotic completeness result for a scalar quasilinear wave equation satisfying the weak null condition. The main tool we use in the study of this equation is the geometric reduced system introduced in arXiv:2002.05355. Starting from a global solution $u$ to the quasilinear wave equation, we rigorously show that well chosen asymptotic variables solve the same reduced system with small error terms. This allows us to recover the scattering data for our system, as well as to construct a matching exact solution to the reduced system., Comment: 114 pages. This paper continues the work in arXiv:2002.05355. v2: introduction revised, typos corrected

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm.

With the prevalence of Internet of Things (IoT) applications, IoT devices interact closely with our surrounding environments, bringing us unparalleled smartness and convenience. However, the development of secure IoT solutions is getting a long way lagged behind, making us exposed to common unauthorized accesses that may bring malicious attacks and unprecedented danger to our daily life. Overprivilege attack, a widely reported phenomenon in IoT that accesses unauthorized or excessive resources, is notoriously hard to prevent, trace and mitigate. To tackle this challenge, we propose Tokoin-Based Access Control (TBAC), an accountable access control model enabled by blockchain and Trusted Execution Environment (TEE) technologies, to offer fine-graininess, strong auditability, and access procedure control for IoT. TBAC materializes the virtual access power into a definite-amount and secure cryptographic coin termed "tokoin" (token+coin), and manages it using atomic and accountable state-transition functions in a blockchain. We also realize access procedure control by mandating every tokoin a fine-grained access policy defining who is allowed to do what at when in where by how. The tokoin is peer-to-peer transferable, and can be modified only by the resource owner when necessary. We fully implement TBAC with well-studied cryptographic primitives and blockchain platforms and present a readily available APP for regular users. We also present a case study to demonstrate how TBAC is employed to enable autonomous in-home cargo delivery while guaranteeing the access policy compliance and home owner's physical security by regulating the physical behaviors of the deliveryman.

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong capabilities of deep learning, we propose a convolutional generative adversarial network-based (Conv-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box malware detectors. Motivated by the rival game between malware authors and malware detectors, MalFox adopts a confrontational approach to produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate adversarial malware examples. To demonstrate the effectiveness of MalFox, we collect a large dataset consisting of both malware and benignware programs, and investigate the performance of MalFox in terms of accuracy, detection rate, and evasive rate of the generated adversarial malware examples. Our evaluation indicates that the accuracy can be as high as 99.0% which significantly outperforms the other 12 well-known learning models. Furthermore, the detection rate is dramatically decreased by 56.8% on average, and the average evasive rate is noticeably improved by up to 56.2%.

Differential privacy is effective in sharing information and preserving privacy with a strong guarantee. As social network analysis has been extensively adopted in many applications, it opens a new arena for the application of differential privacy. In this article, we provide a comprehensive survey connecting the basic principles of differential privacy and applications in social network analysis. We present a concise review of the foundations of differential privacy and the major variants and discuss how differential privacy is applied to social network analysis, including privacy attacks in social networks, types of differential privacy in social network analysis, and a series of popular tasks, such as degree distribution analysis, subgraph counting and edge weights. We also discuss a series of challenges for future studies., Comment: 50 pages,16 figures, 5 tables