Device Heterogeneity in Federated Learning: A Superquantile Approach

• URL: http://arxiv.org/abs/2002.11223v1
• Date: Tue, 25 Feb 2020 23:37:35 GMT
• Title: Device Heterogeneity in Federated Learning: A Superquantile Approach
• Authors: Yassine Laguel, Krishna Pillutla, J\'er\^ome Malick, Zaid Harchaoui
• Abstract summary: We propose a framework to handle heterogeneous client devices which do not conform to the population data distribution. We present an optimization algorithm and establish its convergence to a stationary point. We conclude with numerical experiments on neural networks as well as linear models on tasks from computer vision and natural language processing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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