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1. Weighted Gradient Coding with Leverage Score Sampling

   Chralambides, Neophytos; Pilanci, Mert; Hero, Alfred (January 2020, International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020)

   null (Ed.)

   A major hurdle in machine learning is scalability to massive datasets. Approaches to overcome this hurdle include compression of the data matrix and distributing the computations. Leverage score sampling provides a compressed approximation of a data matrix using an importance weighted subset. Gradient coding has been recently proposed in distributed optimization to compute the gradient using multiple unreliable worker nodes. By designing coding matrices, gradient coded computations can be made resilient to stragglers, which are nodes in a distributed network that degrade system performance. We present a novel weighted leverage score approach, that achieves improved performance for distributed gradient coding by utilizing an importance sampling. 

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2. DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation

   Rajput, Shashank; Wang, Hongyi; Charles, Zachary; Papailiopoulos, Dimitris (July 2019, International Conference on Machine Learning)

   To improve the resilience of distributed training to worst-case, or Byzantine node failures, several recent approaches have replaced gradient averaging with robust aggregation methods. Such techniques can have high computational costs, often quadratic in the number of compute nodes, and only have limited robustness guarantees. Other methods have instead used redundancy to guarantee robustness, but can only tolerate limited number of Byzantine failures. In this work, we present DETOX, a Byzantine-resilient distributed training framework that combines algorithmic redundancy with robust aggregation. DETOX operates in two steps, a filtering step that uses limited redundancy to significantly reduce the effect of Byzantine nodes, and a hierarchical aggregation step that can be used in tandem with any state-of-the-art robust aggregation method. We show theoretically that this leads to a substantial increase in robustness, and has a per iteration runtime that can be nearly linear in the number of compute nodes. We provide extensive experiments over real distributed setups across a variety of large-scale machine learning tasks, showing that DETOX leads to orders of magnitude accuracy and speedup improvements over many state-of-the-art Byzantine-resilient approaches. 

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3. A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms

   Xinwei Zhang, Mingyi Hong (July 2022, International Conference on Machine Learning)

   In modern machine learning systems, distributed algorithms are deployed across applications to ensure data privacy and optimal utilization of computational resources. This work offers a fresh perspective to model, analyze, and design distributed optimization algorithms through the lens of stochastic multi-rate feedback control. We show that a substantial class of distributed algorithms—including popular Gradient Tracking for decentralized learning, and FedPD and Scaffold for federated learning—can be modeled as a certain discrete-time stochastic feedback-control system, possibly with multiple sampling rates. This key observation allows us to develop a generic framework to analyze the convergence of the entire algorithm class. It also enables one to easily add desirable features such as differential privacy guarantees, or to deal with practical settings such as partial agent participation, communication compression, and imperfect communication in algorithm design and analysis. 

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4. Lagrange Coded Computing: Optimal Design for Resiliency, Security, and Privacy

   Yu, Qian; Li, Songze; Raviv, Netanel; Mousavi Kalan, Seyed Mohammadreza; Soltanolkotabi, Mahdi; Avestimehr, Salman (January 2019, International Conference on Artificial Intelligence and Statistics (AISTATS 2019))

   We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms. We propose Lagrange Coded Computing (LCC), a new framework to simultaneously provide (1) resiliency against stragglers that may prolong computations; (2) security against Byzantine (or malicious) workers that deliberately modify the computation for their benefit; and (3) (information-theoretic) privacy of the dataset amidst possible collusion of workers. LCC, which leverages the well-known Lagrange polynomial to create computation redundancy in a novel coded form across workers, can be applied to any computation scenario in which the function of interest is an arbitrary multivariate polynomial of the input dataset, hence covering many computations of interest in machine learning. LCC significantly generalizes prior works to go beyond linear computations. It also enables secure and private computing in distributed settings, improving the computation and communication efficiency of the state-of-the-art. Furthermore, we prove the optimality of LCC by showing that it achieves the optimal tradeoff between resiliency, security, and privacy, i.e., in terms of tolerating the maximum number of stragglers and adversaries, and providing data privacy against the maximum number of colluding workers. Finally, we show via experiments on Amazon EC2 that LCC speeds up the conventional uncoded implementation of distributed least-squares linear regression by up to 13.43×, and also achieves a 2.36×-12.65× speedup over the state-of-the-art straggler mitigation strategies. 

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5. Byzantine-Tolerant Distributed Coordinate Descent

   https://doi.org/10.1109/ISIT.2019.8849217  

   Data, Deepesh; Diggavi, Suhas (July 2019, IEEE International Symposium on Information Theory)

   We study distributed coordinate descent (CD) in the master-worker architecture under adversarial attacks, where the data is partitioned (across the parameter space) and distributed among m worker nodes (t of which can be maliciously corrupt), which update some coordinates of their part of the parameter vector, in parallel and iteratively, using CD updates, with the help of the master. We propose a method based on data encoding and real error correction to combat the adversary. Our method can tolerate up to ⌈m-1/2⌉ corrupt nodes, which is information-theoretically optimal. Our design gives a trade-off between the resiliency t, the required redundancy, and the computation at master and worker nodes. For example, with constant overhead in the storage and computational complexity over that required by the plain distributed CD, we can tolerate up to m/3 corrupt nodes. We design a sparse encoding scheme, which yields low encoding complexity. 

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