A Unified Analysis of Federated Learning with Arbitrary Client Participation

• URL: http://arxiv.org/abs/2205.13648v1
• Date: Thu, 26 May 2022 21:56:31 GMT
• Title: A Unified Analysis of Federated Learning with Arbitrary Client Participation
• Authors: Shiqiang Wang, Mingyue Ji
• Abstract summary: Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency. It is important to understand how partial client participation affects convergence. We provide a unified convergence analysis for FL with arbitrary client participation.
• Score: 33.86606068136201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) faces challenges of intermittent client availability and computation/communication efficiency. As a result, only a small subset of clients can participate in FL at a given time. It is important to understand how partial client participation affects convergence, but most existing works have either considered idealized participation patterns or obtained results with non-zero optimality error for generic patterns. In this paper, we provide a unified convergence analysis for FL with arbitrary client participation. We first introduce a generalized version of federated averaging (FedAvg) that amplifies parameter updates at an interval of multiple FL rounds. Then, we present a novel analysis that captures the effect of client participation in a single term. By analyzing this term, we obtain convergence upper bounds for a wide range of participation patterns, including both non-stochastic and stochastic cases, which match either the lower bound of stochastic gradient descent (SGD) or the state-of-the-art results in specific settings. We also discuss various insights, recommendations, and experimental results.

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