Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data

Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang, Aditya Balu, Ethan Herron, Chinmay Hegde, Soumik Sarkar

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Decentralized learning enables a group of collaborative agents to learn models using a distributed dataset without the need for a central parameter server. Recently, decentralized learning algorithms have demonstrated state-of-the-art results on benchmark data sets, comparable with centralized algorithms. However, the key assumption to achieve competitive performance is that the data is independently and identically distributed (IID) among the agents which, in real-life applications, is often not applicable. Inspired by ideas from continual learning, we propose Cross-Gradient Aggregation (CGA), a novel decentralized learning algorithm where (i) each agent aggregates cross-gradient information, i.e., derivatives of its model with respect to its neighbors' datasets, and (ii) updates its model using a projected gradient based on quadratic programming (QP). We theoretically analyze the convergence characteristics of CGA and demonstrate its efficiency on non-IID data distributions sampled from the MNIST and CIFAR-10 datasets. Our empirical comparisons show superior learning performance of CGA over existing state-of-the-art decentralized learning algorithms, as well as maintaining the improved performance under information compression to reduce peer-to-peer communication overhead. The code is available here on GitHub.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 38th International Conference on Machine Learning, ICML 2021
    PublisherML Research Press
    Number of pages11
    ISBN (Electronic)9781713845065
    StatePublished - 2021
    Event38th International Conference on Machine Learning, ICML 2021 - Virtual, Online
    Duration: Jul 18 2021Jul 24 2021

    Publication series

    NameProceedings of Machine Learning Research
    ISSN (Electronic)2640-3498


    Conference38th International Conference on Machine Learning, ICML 2021
    CityVirtual, Online

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Control and Systems Engineering
    • Statistics and Probability


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