Machine learning algorithms are typically run on large scale, distributed compute infrastructure that routinely face a number of unavailabilities such as failures and temporary slowdowns. Adding redundant computations using coding-theoretic tools called "codes" is an emerging technique to alleviate the adverse effects of such unavailabilities. A code consists of an encoding function that proactively introduces redundant computation and a decoding function that reconstructs unavailable outputs using the available ones. Past work focuses on using codes to provide resilience for linear computations and specific iterative optimization algorithms. However, computations performed for a variety of applications including inference on state-of-the-art machine learning algorithms, such as neural networks, typically fall outside this realm. In this paper, we propose taking a learning-based approach to designing codes that can handle non-linear computations. We present carefully designed neural network architectures and a training methodology for learning encoding and decoding functions that produce approximate reconstructions of unavailable computation results. We present extensive experimental results demonstrating the effectiveness of the proposed approach: we show that the our learned codes can accurately reconstruct $64 - 98\%$ of the unavailable predictions from neural-network based image classifiers on the MNIST, Fashion-MNIST, and CIFAR-10 datasets. To the best of our knowledge, this work proposes the first learning-based approach for designing codes, and also presents the first coding-theoretic solution that can provide resilience for any non-linear (differentiable) computation. Our results show that learning can be an effective technique for designing codes, and that learned codes are a highly promising approach for bringing the benefits of coding to non-linear computations.
A Learning-Based Approach to Approximate Coded Computation
Technische Universität Berlin（柏林工业大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；University of Tübingen（图宾根大学）；University of Melbourne（墨尔本大学）
Coded Computation across Shared Heterogeneous Workers with Communication Delay
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Imperial College London（伦敦帝国理工学院）
Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
Galician Research and Development Center in Advanced Telecommunications (GRADIANT)（加利西亚先进电信研究与发展中心（GRADIANT））；atlanTTic, Information & Computing Lab (ICLAB), Telecommunication Engineering School, Universidade de Vigo（atlanTTic，信息与计算实验室（ICLAB），电信工程学校，维戈大学）
Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing
University of California, San Diego（加州大学圣迭戈分校）；San Diego State University（圣迭戈州立大学）；University of Puerto Rico at Mayagüez（波多黎各大学马亚圭斯分校）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of North Texas（北德克萨斯大学）
