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In Federated Learning (FL), common privacy-enhancing techniques, such as secure aggregation and distributed differential privacy, rely on the critical assumption of an honest majority among participants to withstand various attacks. In practice, however, servers are not always trusted, and an adversarial server can strategically select compromised clients to create a dishonest majority, thereby undermining the system's security guarantees. In this paper, we present Lotto, an FL system that addresses this fundamental, yet underexplored issue by providing secure participant selection against an adversarial server. Lotto supports two selection algorithms: random and informed. To ensure random selection without a trusted server, Lotto enables each client to autonomously determine their participation using verifiable randomness. For informed selection, which is more vulnerable to manipulation, Lotto approximates the algorithm by employing random selection within a refined client pool. Our theoretical analysis shows that Lotto effectively aligns the proportion of server-selected compromised participants with the base rate of dishonest clients in the population. Large-scale experiments further reveal that Lotto achieves time-to-accuracy performance comparable to that of insecure selection methods, indicating a low computational overhead for secure selection., Comment: This article has been accepted to USENIX Security '24

The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text description. Specifically, the goal of the competition is to increase the accessibility and usability of optimization solvers by allowing non-experts to interface with them using natural language. We separate this challenging goal into two sub-tasks: (1) recognize and label the semantic entities that correspond to the components of the optimization problem; (2) generate a meaning representation (i.e., a logical form) of the problem from its detected problem entities. The first task aims to reduce ambiguity by detecting and tagging the entities of the optimization problems. The second task creates an intermediate representation of the linear programming (LP) problem that is converted into a format that can be used by commercial solvers. In this report, we present the LP word problem dataset and shared tasks for the NeurIPS 2022 competition. Furthermore, we investigate and compare the performance of the ChatGPT large language model against the winning solutions. Through this competition, we hope to bring interest towards the development of novel machine learning applications and datasets for optimization modeling.

Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy. However, the substantial communication overhead of FL makes training challenging when edge devices have limited network bandwidth. Existing work to optimize FL bandwidth overlooks downstream transmission and does not account for FL client sampling. In this paper we propose GlueFL, a framework that incorporates new client sampling and model compression algorithms to mitigate low download bandwidths of FL clients. GlueFL prioritizes recently used clients and bounds the number of changed positions in compression masks in each round. Across three popular FL datasets and three state-of-the-art strategies, GlueFL reduces downstream client bandwidth by 27% on average and reduces training time by 29% on average.

We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enables the users to validate and edit the suggestions. We investigate controlled generation techniques to obtain an automatic suggestion of formulation. Then, we evaluate their effectiveness with a newly created dataset of linear programming problems drawn from various application domains., Comment: Accepted for presentation at the EMNLP 2022 Conference (industry track)

Compared with full client participation, partial client participation is a more practical scenario in federated learning, but it may amplify some challenges in federated learning, such as data heterogeneity. The lack of inactive clients' updates in partial client participation makes it more likely for the model aggregation to deviate from the aggregation based on full client participation. Training with large batches on individual clients is proposed to address data heterogeneity in general, but their effectiveness under partial client participation is not clear. Motivated by these challenges, we propose to develop a novel federated learning framework, referred to as FedAMD, for partial client participation. The core idea is anchor sampling, which separates partial participants into anchor and miner groups. Each client in the anchor group aims at the local bullseye with the gradient computation using a large batch. Guided by the bullseyes, clients in the miner group steer multiple near-optimal local updates using small batches and update the global model. By integrating the results of the two groups, FedAMD is able to accelerate the training process and improve the model performance. Measured by $\epsilon$-approximation and compared to the state-of-the-art methods, FedAMD achieves the convergence by up to $O(1/\epsilon)$ fewer communication rounds under non-convex objectives. Empirical studies on real-world datasets validate the effectiveness of FedAMD and demonstrate the superiority of the proposed algorithm: Not only does it considerably save computation and communication costs, but also the test accuracy significantly improves., Comment: ICML 2023

Traditional one-bit compressed stochastic gradient descent can not be directly employed in multi-hop all-reduce, a widely adopted distributed training paradigm in network-intensive high-performance computing systems such as public clouds. According to our theoretical findings, due to the cascading compression, the training process has considerable deterioration on the convergence performance. To overcome this limitation, we implement a sign-bit compression-based learning synchronization framework, Marsit. It prevents cascading compression via an elaborate bit-wise operation for unbiased sign aggregation and its specific global compensation mechanism for mitigating compression deviation. The proposed framework retains the same theoretical convergence rate as non-compression mechanisms. Experimental results demonstrate that Marsit reduces up to 35% training time while preserving the same accuracy as training without compression., Comment: Accepted by DAC 2022

We raise a detuning-dependent loss mechanism to describe the soliton formation dynamics when the lumped filtering operation is manipulated in anomalous group velocity dispersion regime, using stability analysis of generalized Lugiato-Lefever equation., Comment: 3 pages, 2 figures

Communication is a crucial phase in the context of distributed training. Because parameter server (PS) frequently experiences network congestion, recent studies have found that training paradigms without a centralized server outperform the traditional server-based paradigms in terms of communication efficiency. However, with the increasing growth of model sizes, these server-free paradigms are also confronted with substantial communication overhead that seriously deteriorates the performance of distributed training. In this paper, we focus on communication efficiency of two serverless paradigms, i.e., Ring All-Reduce (RAR) and gossip, by proposing the Quantized Parallel Restarted Stochastic Gradient Descent (QPRSGD), an algorithm that allows multiple local SGD updates before a global synchronization, in synergy with the quantization to significantly reduce the communication overhead. We establish the bound of accumulative errors according to the synchronization mode and the network topology, which is essential to ensure the convergence property. Under both aggregation paradigms, the algorithm achieves the linear speedup property with respect to the number of local updates as well as the number of workers. Remarkably, the proposed algorithm achieves a convergence rate $O(1/\sqrt{NK^2M})$ under the gossip paradigm and outperforms all existing compression methods, where $N$ is the times of global synchronizations, and $K$ is the number of local updates, while $M$ is the number of nodes. An empirical study on various machine learning models demonstrates that the communication overhead is reduced by 90\%, and the convergence speed is boosted by up to 18.6 times, in a low bandwidth network, in comparison with Parallel SGD.