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Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However, the data quality of client datasets can not be guaranteed since corresponding annotations of different clients often contain complex label noise of varying degrees, which inevitably causes the performance degradation. Intuitively, the performance degradation is dominated by clients with higher noise rates since their trained models contain more misinformation from data, thus it is necessary to devise an effective optimization scheme to mitigate the negative impacts of these noisy clients. In this work, we propose a two-stage framework FedELC to tackle this complicated label noise issue. The first stage aims to guide the detection of noisy clients with higher label noise, while the second stage aims to correct the labels of noisy clients' data via an end-to-end label correction framework which is achieved by learning possible ground-truth labels of noisy clients' datasets via back propagation. We implement sixteen related methods and evaluate five datasets with three types of complicated label noise scenarios for a comprehensive comparison. Extensive experimental results demonstrate our proposed framework achieves superior performance than its counterparts for different scenarios. Additionally, we effectively improve the data quality of detected noisy clients' local datasets with our label correction framework. The code is available at https://github.com/Sprinter1999/FedELC., Comment: To appear in ACM CIKM'24 full research paper track

Federated Distillation (FD) offers an innovative approach to distributed machine learning, leveraging knowledge distillation for efficient and flexible cross-device knowledge transfer without necessitating the upload of extensive model parameters to a central server. While FD has gained popularity, its vulnerability to poisoning attacks remains underexplored. To address this gap, we previously introduced FDLA (Federated Distillation Logits Attack), a method that manipulates logits communication to mislead and degrade the performance of client models. However, the impact of FDLA on participants with different identities and the effects of malicious modifications at various stages of knowledge transfer remain unexplored. To this end, we present PCFDLA (Peak-Controlled Federated Distillation Logits Attack), an advanced and more stealthy logits poisoning attack method for FD. PCFDLA enhances the effectiveness of FDLA by carefully controlling the peak values of logits to create highly misleading yet inconspicuous modifications. Furthermore, we introduce a novel metric for better evaluating attack efficacy, demonstrating that PCFDLA maintains stealth while being significantly more disruptive to victim models compared to its predecessors. Experimental results across various datasets confirm the superior impact of PCFDLA on model accuracy, solidifying its potential threat in federated distillation systems., Comment: arXiv admin note: text overlap with arXiv:2401.03685

Dopants play an important role in improving electrical and thermal transport. In the traditional perspective, a dopant suppresses lattice thermal conductivity kL by adding point defect (PD) scattering term to the phonon relaxation time, which has been adopted for decades. In this study, we propose an innovative perspective to solve the kL of defective systems-the holistic approach, i.e., treating dopant and matrix as a holism. This approach allows us to handle the influences from defects explicitly by the calculations of defective systems, about their changed phonon dispersion, phonon-phonon and electron-phonon interaction, etc, due to the existence of dopants. The kL reduction between defective MxNb1-xFeSb (M=V, Ti) and NbFeSb is used as an example for the holistic approach, and comparable results with experiments are obtained. It is notable that light elemental dopants also induced the avoided-crossing behavior. It can be further rationalized by a one-dimensional atomic chain model. The mass and force constant imbalance generally generates the avoided-crossing phonons, mathematically in a similar way as the coefficients in traditional PD scattering, but along a different direction in kL reduction. Our work provides another perspective for understanding the mechanism of dopants influence in material's thermal transport.

Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model training with limited and uncertain communication. In this paper, we introduce FedCache 2.0, a novel personalized FEL architecture that simultaneously addresses these challenges. FedCache 2.0 incorporates the benefits of both dataset distillation and knowledge cache-driven federated learning by storing and organizing distilled data as knowledge in the server-side knowledge cache. Moreover, a device-centric cache sampling strategy is introduced to tailor transferred knowledge for individual devices within controlled communication bandwidth. Extensive experiments on five datasets covering image recognition, audio understanding, and mobile sensor data mining tasks demonstrate that (1) FedCache 2.0 significantly outperforms state-of-the-art methods regardless of model structures, data distributions, and modalities. (2) FedCache 2.0 can train splendid personalized on-device models with at least $\times$28.6 improvement in communication efficiency., Comment: 17 pages, 7 figures, 14 tables

On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI inference without relying on remote servers. However, training models for on-device deployment face significant challenges due to the decentralized and privacy-sensitive nature of users' data, along with end-side constraints related to network connectivity, computation efficiency, etc. Existing training paradigms, such as cloud-based training, federated learning, and transfer learning, fail to sufficiently address these practical constraints that are prevalent for devices. To overcome these challenges, we propose Privacy-Enhanced Training-as-a-Service (PTaaS), a novel service computing paradigm that provides privacy-friendly, customized AI model training for end devices. PTaaS outsources the core training process to remote and powerful cloud or edge servers, efficiently developing customized on-device models based on uploaded anonymous queries, enhancing data privacy while reducing the computation load on individual devices. We explore the definition, goals, and design principles of PTaaS, alongside emerging technologies that support the PTaaS paradigm. An architectural scheme for PTaaS is also presented, followed by a series of open problems that set the stage for future research directions in the field of PTaaS., Comment: 7 pages, 3 figures

The existing federated learning (FL) methods for spatio-temporal forecasting fail to capture the inherent spatio-temporal heterogeneity, which calls for personalized FL (PFL) methods to model the spatio-temporally variant patterns. While contrastive learning approach is promising in addressing spatio-temporal heterogeneity, the existing methods are noneffective in determining negative pairs and can hardly apply to PFL paradigm. To tackle this limitation, we propose a novel PFL method, named Federated dUal sEmantic aLignment-based contraStive learning (FUELS), which can adaptively align positive and negative pairs based on semantic similarity, thereby injecting precise spatio-temporal heterogeneity into the latent representation space by auxiliary contrastive tasks. From temporal perspective, a hard negative filtering module is introduced to dynamically align heterogeneous temporal representations for the supplemented intra-client contrastive task. From spatial perspective, we design lightweight-but-efficient prototypes as client-level semantic representations, based on which the server evaluates spatial similarity and yields client-customized global prototypes for the supplemented inter-client contrastive task. Extensive experiments demonstrate that FUELS outperforms state-of-the-art methods, with communication cost decreasing by around 94%.

Modern particle physics experiments usually rely on highly complex and large-scale spectrometer devices. In high energy physics experiments, visualization helps detector design, data quality monitoring, offline data processing, and has great potential for improving physics analysis. In addition to the traditional physics data analysis based on statistical methods, visualization provides unique intuitive advantages in searching for rare signal events and reducing background noises. By applying the event display tool to several physics analyses in the BESIII experiment, we demonstrate that visualization can benefit potential physics discovery and improve the signal significance. With the development of modern visualization techniques, it is expected to play a more important role in future data processing and physics analysis of particle physics experiments., Comment: 19 pages, 7 figures

Angular momenta of electromagnetic waves are important both in concepts and applications. In this work, we systematically discuss two types of angular momenta, i.e., spin angular momentum and orbital angular momentum in various cases, e.g., with source and without source, in classical and quantum forms. Numerical results demonstrating how to extract the topological charge of a classical vortex beam by spectral method are also presented., Comment: 5 pages, 3 figures

Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cross-device knowledge transfer in federated learning. By optimizing local models with knowledge distillation, FD circumvents the necessity of uploading large-scale model parameters to the central server, simultaneously preserving the raw data on local clients. Despite the growing popularity of FD, there is a noticeable gap in previous works concerning the exploration of poisoning attacks within this framework. This can lead to a scant understanding of the vulnerabilities to potential adversarial actions. To this end, we introduce FDLA, a poisoning attack method tailored for FD. FDLA manipulates logit communications in FD, aiming to significantly degrade model performance on clients through misleading the discrimination of private samples. Through extensive simulation experiments across a variety of datasets, attack scenarios, and FD configurations, we demonstrate that LPA effectively compromises client model accuracy, outperforming established baseline algorithms in this regard. Our findings underscore the critical need for robust defense mechanisms in FD settings to mitigate such adversarial threats., Comment: 13 pages, 3 figures, 5 tables

Fusing photon pairs creates an arena where indistinguishability can exist between two two-photon amplitudes contributing to the same joint photodetection event. This two-photon interference has been extensively utilized in creating multiphoton entanglement, from passive to scalable generation, from bulk-optical to chip-scale implementations. While significant, no experimental evidence exists that the full capability of photon fusion can be utterly quantified like a quantum entity. Herein, we demonstrate the first complete capability quantification of experimental photon fusion. Our characterization faithfully measures the whole abilities of photon fusion in the experiment to create and preserve entangled photon pairs. With the created four- and six-photon entangled states using spontaneous parametric down-conversion entanglement sources, we show that capability quantification provides a faithful assessment of interferometry for generating genuine multiphoton entanglement and Einstein-Podolsky-Rosen steering. These results reveal a practical diagnostic method to benchmark photon fusion underlying the primitive operations in general quantum photonics devices and networks.

Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data. Mainstream FL methodologies overlook the dynamic nature of real-world data, particularly its tendency to grow in volume and diversify in classes over time. This oversight results in FL methods suffering from catastrophic forgetting, where the trained models inadvertently discard previously learned information upon assimilating new data. In response to this challenge, we propose a novel Federated Class-Incremental Learning (FCIL) method, named \underline{Fed}erated \underline{C}lass-Incremental \underline{L}earning with New-Class \underline{A}ugmented \underline{S}elf-Di\underline{S}tillation (FedCLASS). The core of FedCLASS is to enrich the class scores of historical models with new class scores predicted by current models and utilize the combined knowledge for self-distillation, enabling a more sufficient and precise knowledge transfer from historical models to current models. Theoretical analyses demonstrate that FedCLASS stands on reliable foundations, considering scores of old classes predicted by historical models as conditional probabilities in the absence of new classes, and the scores of new classes predicted by current models as the conditional probabilities of class scores derived from historical models. Empirical experiments demonstrate the superiority of FedCLASS over four baseline algorithms in reducing average forgetting rate and boosting global accuracy., Comment: 9 pages, 2 figures, 4 tables

A deep learning-assisted inversion method is proposed to solve the inhomogeneous background imaging problem. Three non-iterative methods, namely the distorted-Born (DB) major current coefficients method, the DB modified Born approximation method, and the DB connection method, are introduced to address the inhomogeneous background inverse scattering problem. These methods retain the multiple scattering information by utilizing the major current obtained through singular value decomposition of the Green's function and the scattered field, without resourcing to optimization techniques. As a result, the proposed methods offer improved reconstruction resolution and accuracy for unknown objects embedded in inhomogeneous backgrounds, surpassing the backpropagation scheme (BPS) and Born approximation (BA) method that disregard the multiple scattering effect. To further enhance the resolution and accuracy of the reconstruction, a Shifted-Window (Swin) transformer network is employed for capturing super-resolution information in the images. The attention mechanism incorporated in the shifted window facilitates global interactions between objects, thereby enhancing the performance of the inhomogeneous background imaging algorithm while reducing computational complexity. Moreover, an adaptive training method is proposed to enhance the generalization ability of the network. The effectiveness of the proposed methods is demonstrated through both synthetic data and experimental data. Notably, super-resolution imaging is achieved with quasi real-time speed, indicating promising application potential for the proposed algorithms., Comment: We have submitted this paper to TGRS(IEEE Transactionson Geoscience andRemote Sensing) on 29-Jan-2023; and resubmitted on 12-Jul-2023

As an emerging federated learning paradigm, federated distillation enables communication-efficient model training by transmitting only small-scale knowledge during the learning process. To further improve the communication efficiency of federated distillation, we propose a novel technique, ALU, which accumulates multiple rounds of local updates before transferring the knowledge to the central server. ALU drastically decreases the frequency of communication in federated distillation, thereby significantly reducing the communication overhead during the training process. Empirical experiments demonstrate the substantial effect of ALU in improving the communication efficiency of federated distillation., Comment: 2 pages, 3 figures

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a combination of cloud, edge, and end devices, FL can benefit from this End-Edge-Cloud Collaboration (EECC) paradigm to achieve collaborative device-scale expansion with real-time access. Although Hierarchical Federated Learning (HFL) supports multi-tier model aggregation suitable for EECC, prior works assume the same model structure on all computing nodes, constraining the model scale by the weakest end devices. To address this issue, we propose Agglomerative Federated Learning (FedAgg), which is a novel EECC-empowered FL framework that allows the trained models from end, edge, to cloud to grow larger in size and stronger in generalization ability. FedAgg recursively organizes computing nodes among all tiers based on Bridge Sample Based Online Distillation Protocol (BSBODP), which enables every pair of parent-child computing nodes to mutually transfer and distill knowledge extracted from generated bridge samples. This design enhances the performance by exploiting the potential of larger models, with privacy constraints of FL and flexibility requirements of EECC both satisfied. Experiments under various settings demonstrate that FedAgg outperforms state-of-the-art methods by an average of 4.53\% accuracy gains and remarkable improvements in convergence rate., Comment: Accepted by IEEE International Conference on Computer Communications (INFOCOM), 2024

Federated learning (FL) aims to collaboratively train a shared model across multiple clients without transmitting their local data. Data heterogeneity is a critical challenge in realistic FL settings, as it causes significant performance deterioration due to discrepancies in optimization among local models. In this work, we focus on label distribution skew, a common scenario in data heterogeneity, where the data label categories are imbalanced on each client. To address this issue, we propose FedBalance, which corrects the optimization bias among local models by calibrating their logits. Specifically, we introduce an extra private weak learner on the client side, which forms an ensemble model with the local model. By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category. Therefore, the optimization direction of local models can be improved by increasing the penalty for misclassifying minority classes and reducing the attention to majority classes, resulting in a better global model. Extensive experiments show that our method can gain 13\% higher average accuracy compared with state-of-the-art methods., Comment: 9 pages, 4 figures, 4 tables

The performance of holographic multiple-input multiple-output (MIMO) communications, employing two-dimensional (2-D) planar antenna arrays, is typically compromised by finite degrees-of-freedom (DOF) stemming from limited array size. The DOF constraint becomes significant when the element spacing approaches approximately half a wavelength, thereby restricting the overall performance of MIMO systems. To break this inherent limitation, we propose a novel three-dimensional (3-D) antenna array that strategically explores the untapped vertical dimension. We investigate the performance of MIMO systems utilizing 3-D arrays across different multi-path scenarios, encompassing Rayleigh channels with varying angular spreads and the 3rd generation partnership project (3GPP) channels. We subsequently showcase the advantages of these 3-D arrays over their 2-D counterparts with the same aperture sizes. As a proof of concept, a practical dipole-based 3-D array, facilitated by an electromagnetic band-gap (EBG) reflecting surface, is conceived, constructed, and evaluated. The experimental results align closely with full-wave simulations, and channel simulations substantiate that the DOF and capacity constraints of traditional holographic MIMO systems can be surpassed by adopting such a 3-D array configuration.

The study of ion dissolution from metal surfaces has a long-standing history, wherein the gradual dissolution of solute atoms with increasing electrode potential, leading to their existence as ions in the electrolyte with integer charges, is well-known. However, our present work reveals a more intricate and nuanced physical perspective based on comprehensive first-principles/continuum calculations. We investigate the dissolution and deposition processes of 22 metal elements across a range of applied electrode potentials, unveiling diverse dissolution models. By analyzing the energy profiles and valence states of solute atoms as a function of the distance between the solute atom and metal surface, we identify three distinct dissolution models for different metals. Firstly, solute atoms exhibit an integer valence state following an integer-valence jump, aligning with classical understandings. Secondly, solute atoms attain an eventual integer valence, yet their valence state increases in a non-integer manner during dissolution. Lastly, we observe solute atoms exhibiting a non-integer valence state, challenging classical understandings. Furthermore, we propose a theoretical criterion for determining the selection of ion valence during electrode dissolution under applied potential. These findings not only contribute to a deeper understanding of the dissolution process but also offer valuable insights into the complex dynamics governing metal ion dissolution at the atomic level. Such knowledge has the potential to advance the design of more efficient electrochemical systems and open new avenues for controlling dissolution processes in various applications., Comment: still dont have

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which causes unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache., Comment: Accepted by IEEE Transactions on Mobile Computing (TMC)

Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the downlink in wireless networks, which causes a severe uplink communication bottleneck. A prominent direction to alleviate this problem is federated dropout, which drops fractional weights of local models. However, existing federated dropout studies focus on random or ordered dropout and lack theoretical support, resulting in unguaranteed performance. In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss. By applying FedBIAD, each client adaptively selects a high-quality dropping pattern with accurate approximations and only transmits parameters of non-dropped weight rows to mitigate uplink costs while improving accuracy. Theoretical analysis demonstrates that the convergence rate of the average generalization error of FedBIAD is minimax optimal up to a squared logarithmic factor. Extensive experiments on image classification and next-word prediction show that compared with status quo approaches, FedBIAD provides 2x uplink reduction with an accuracy increase of up to 2.41% even on non-Independent and Identically Distributed (non-IID) data, which brings up to 72% decrease in training time.

We report the radio observations of the eclipsing black widow pulsar J1720-0534, a 3.26 ms pulsar in orbit with a low mass companion of mass 0.029 to 0.034 M$_{\odot}$. We obtain the phase-connected timing ephemeris and polarization profile of this millisecond pulsar (MSP) using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), the Green Bank Telescope (GBT), and the Parkes Telescope. For the first time from such a system, an oscillatory polarisation angle change was observed from a particular eclipse egress with partial depolarization, indicating 10-milliGauss-level reciprocating magnetic fields oscillating in a length scale of 5000 km (assuming an orbital inclination angle of 90 degrees) outside the companion's magnetosphere. The dispersion measure variation observed during the ingresses and egresses shows the rapid raising of the electron density in the shock boundary between the companion's magnetosphere and the surrounding pulsar wind. We suggest that the observed oscillatory magnetic fields originate from the pulsar wind outside the companion's magnetosphere., Comment: 15 pages, 8 figures, 1 table, accepted by RAA

In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent model updates, which evidently slow down model convergence. To alleviate this issue, many methods employ regularization terms to narrow the discrepancy between client-side local models and the server-side global model. However, these methods impose limitations on the ability to explore superior local models and ignore the valuable information in historical models. Besides, although the up-to-date representation method simultaneously concerns the global and historical local models, it suffers from unbearable computation cost. To accelerate convergence with low resource consumption, we innovatively propose a model regularization method named FedTrip, which is designed to restrict global-local divergence and decrease current-historical correlation for alleviating the negative effects derived from data heterogeneity. FedTrip helps the current local model to be close to the global model while keeping away from historical local models, which contributes to guaranteeing the consistency of local updates among clients and efficiently exploring superior local models with negligible additional computation cost on attaching operations. Empirically, we demonstrate the superiority of FedTrip via extensive evaluations. To achieve the target accuracy, FedTrip outperforms the state-of-the-art baselines in terms of significantly reducing the total overhead of client-server communication and local computation., Comment: 11 pages, to be published in the 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

Quantum networks comprised of entangled end nodes serve stronger than the classical correlation for unparalleled quantum internet applications. However, practical quantum networking is affected by noise, which at its worst, causes end nodes to be described by pre-existing classical data. In such untrusted networks, determining quantum network fidelity and genuine multi-node entanglement becomes crucial. Here, we show that determining quantum network fidelity and genuine $N$-node entanglement in an untrusted star network requires only $N+1$ measurement settings. This method establishes a semi-trusted framework, allowing some nodes to relax their assumptions. Our network determination method is enabled by detecting genuine $N$-node Einstein-Podolsky-Rosen steerability. Experimentally, using spontaneous parametric down-conversion entanglement sources, we demonstrate the determinations of genuine 3-photon and 4-photon quantum networks and the false positives of the widely used entanglement witness, the fidelity criterion of $1/2$. Our results provide a scalable method for the determination of multipartite entanglement in realistic quantum networks.

Benefitting from UAVs' characteristics of flexible deployment and controllable movement in 3D space, odor source localization with multiple UAVs has been a hot research area in recent years. Considering the limited resources and insufficient battery capacities of UAVs, it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states. To this end, we propose a multi-UAV collaboration based odor source localization (\textit{MUC-OSL}) method, where source estimation and UAV navigation are iteratively performed, aiming to accelerate the searching process and reduce the resource consumption of UAVs. Specifically, in the source estimation phase, we present a collaborative particle filter algorithm on the basis of UAVs' cognitive difference and Gaussian fitting to improve source estimation accuracy. In the following navigation phase, an adaptive path planning algorithm is designed based on Partially Observable Markov Decision Process (POMDP) to distributedly determine the subsequent flying direction and moving steps of each UAV. The results of experiments conducted on two simulation platforms demonstrate that \textit{MUC-OSL} outperforms existing efforts in terms of mean search time and success rate, and effectively reduces the resource consumption of UAVs., Comment: 15 pages

Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems (ITS). To mitigate communication burden and tackle with the problem of privacy leakage aroused by centralized forecasting methods, Federated Learning (FL) has been applied to TFF. However, existing FL-based approaches employ batch learning manner, which makes the pre-trained models inapplicable to subsequent traffic data, thus exhibiting subpar prediction performance. In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation. Specifically, clients employ Gated Recurrent Unit (GRU)-based encoders to obtain the internal temporal patterns inside traffic data sequences. Then, the central server evaluates spatial correlation among clients via Graph Attention Network (GAT), catering to the dynamic changes of spatial closeness caused by traffic fluctuation. Furthermore, to improve the generalization of the global model for upcoming traffic data, a period-aware aggregation mechanism is proposed to aggregate the local models which are optimized using Online Gradient Descent (OGD) algorithm at clients. We perform comprehensive experiments on two real-world datasets to validate the efficiency and effectiveness of our proposed method and the numerical results demonstrate the superiority of FedOSTC.

The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data. Limited by device hardware, diverse user behaviors and network infrastructure, the algorithm design of FEL faces challenges related to resources, personalization and network environments. Fortunately, Knowledge Distillation (KD) has been leveraged as an important technique to tackle the above challenges in FEL. In this paper, we investigate the works that KD applies to FEL, discuss the limitations and open problems of existing KD-based FEL approaches, and provide guidance for their real deployment., Comment: 13 pages, 1 figure, 2 tables

The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma, Federated MultI-task Distillation for Multi-access Edge CompuTing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Experiments on three datasets show that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT., Comment: Accepted by IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS

5G is destined to be supporting large deployment of Industrial IoT (IIoT) with the characteristics of ultra-high densification and low latency. 5G utilizes a more intelligent architecture, with Radio Access Networks (RANs) no longer constrained by base station proximity or proprietary infrastructure. The 3rd Generation Partnership Project (3GPP) covers telecommunication technologies including RAN, core transport networks and service capabilities. Open RAN Alliance (O-RAN) aims to define implementation and deployment architectures, focusing on open-source interfaces and functional units to further reduce the cost and complexity. O-RAN based 5G networks could use components from different hardware and software vendors, promoting vendor diversity, interchangeability and 5G supply chain resiliency. Both 3GPP and O-RAN 5G have to manage the security and privacy challenges that arose from the deployment. Many existing research studies have addressed the threats and vulnerabilities within each system. 5G also has the overwhelming challenges in compliance with privacy regulations and requirements which mandate the user identifiable information need to be protected. In this paper, we look into the 3GPP and O-RAN 5G security and privacy designs and the identified threats and vulnerabilities. We also discuss how to extend the Zero Trust Model to provide advanced protection over 5G air interfaces and network components.

Compared with a single-input-single-output (SISO) wireless communication system, the benefit of multiple-input-multiple-output (MIMO) technology originates from its extra degree of freedom (DOF), also referred as scattering channels or spatial electromagnetic (EM) modes, brought by spatial multiplexing. When the physical sizes of transmitting and receiving arrays are fixed, and there are sufficient antennas (typically with half-wavelength spacings), the DOF limit is only dependent on the propagating environment. Analytical methods can be used to estimate this limit in free space, and some approximate models are adopted in stochastic environments, such as Clarke's model and Ray-tracing methods. However, this DOF limit in an certain inhomogeneous environment has not been well discussed with rigorous full-wave numerical methods. In this work, volume integral equation (VIE) is implemented for investigating the limit of MIMO effective degree of freedom (EDOF) in three representative two-dimensional (2-D) inhomogeneous environments. Moreover, we clarify the relation between the performance of a MIMO system and the scattering characteristics of its propagating environment.

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, since high-quality labeled data require expensive human intelligence and efforts, data with incorrect labels (called noisy labels) are ubiquitous in reality, which inevitably cause performance degradation. Although a lot of methods are proposed to directly deal with noisy labels, these methods either require excessive computation overhead or violate the privacy protection principle of FL. To this end, we focus on this issue in FL with the purpose of alleviating performance degradation yielded by noisy labels meanwhile guaranteeing data privacy. Specifically, we propose a Local Self-Regularization method, which effectively regularizes the local training process via implicitly hindering the model from memorizing noisy labels and explicitly narrowing the model output discrepancy between original and augmented instances using self distillation. Experimental results demonstrate that our proposed method can achieve notable resistance against noisy labels in various noise levels on three benchmark datasets. In addition, we integrate our method with existing state-of-the-arts and achieve superior performance on the real-world dataset Clothing1M. The code is available at https://github.com/Sprinter1999/FedLSR., Comment: To appear in ACM CIKM 2022

Detector and event visualization are essential parts of the software used in high-energy physics (HEP) experiments. Modern visualization techniques and multimedia production platforms such as Unity provide impressive display effects and professional extensions for visualization in HEP experiments. In this study, a method for automatic detector description transformation is presented, which can convert the complicated HEP detector geometry from GDML in offline software to 3D modeling in Unity. The method was successfully applied in the BESIII experiment and can be further developed into applications such as event displays, data monitoring, or virtual reality. It has great potential in detector design, offline software development, physics analysis, and outreach for next-generation HEP experiments as well as applications in nuclear techniques for the industry., Comment: 10 pages,10 figures

Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from clients without assembling their private data. Constrained communication and personalization requirements pose severe challenges to FL. Federated distillation (FD) is proposed to simultaneously address the above two problems, which exchanges knowledge between the server and clients, supporting heterogeneous local models while significantly reducing communication overhead. However, most existing FD methods require a proxy dataset, which is often unavailable in reality. A few recent proxy-data-free FD approaches can eliminate the need for additional public data, but suffer from remarkable discrepancy among local knowledge due to client-side model heterogeneity, leading to ambiguous representation on the server and inevitable accuracy degradation. To tackle this issue, we propose a proxy-data-free FD algorithm based on distributed knowledge congruence (FedDKC). FedDKC leverages well-designed refinement strategies to narrow local knowledge differences into an acceptable upper bound, so as to mitigate the negative effects of knowledge incongruence. Specifically, from perspectives of peak probability and Shannon entropy of local knowledge, we design kernel-based knowledge refinement (KKR) and searching-based knowledge refinement (SKR) respectively, and theoretically guarantee that the refined-local knowledge can satisfy an approximately-similar distribution and be regarded as congruent. Extensive experiments conducted on three common datasets demonstrate that our proposed FedDKC significantly outperforms the state-of-the-art on various heterogeneous settings while evidently improving the convergence speed., Comment: Accepted by ACM Transactions on Intelligent Systems and Technology