Papers › Device Heterogeneity in Federated Learning: A Superquantile Approach

Device Heterogeneity in Federated Learning: A Superquantile Approach

25 Feb 2020arXiv preprint 2020 2arXiv:2002.11223archive 2025-07-28

Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaid Harchaoui

We propose a federated learning framework to handle heterogeneous client devices which do not conform to the population data distribution. The approach hinges upon a parameterized superquantile-based objective, where the parameter ranges over levels of conformity. We present an optimization algorithm and establish its convergence to a stationary point. We show how to practically implement it using secure aggregation by interleaving iterations of the usual federated averaging method with device filtering. We conclude with numerical experiments on neural networks as well as linear models on tasks from computer vision and natural language processing.

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