Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning

• URL: http://arxiv.org/abs/2406.01115v1
• Date: Mon, 3 Jun 2024 08:48:49 GMT
• Title: Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning
• Authors: Kai Yi, Timur Kharisov, Igor Sokolov, Peter Richtárik,
• Abstract summary: We investigate whether it is possible to squeeze more juice" out of each cohort than what is possible in a single communication round. Our approach leads to up to 74% reduction in the total communication cost needed to train a FL model in the cross-device setting.
• Score: 51.560590617691005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Virtually all federated learning (FL) methods, including FedAvg, operate in the following manner: i) an orchestrating server sends the current model parameters to a cohort of clients selected via certain rule, ii) these clients then independently perform a local training procedure (e.g., via SGD or Adam) using their own training data, and iii) the resulting models are shipped to the server for aggregation. This process is repeated until a model of suitable quality is found. A notable feature of these methods is that each cohort is involved in a single communication round with the server only. In this work we challenge this algorithmic design primitive and investigate whether it is possible to ``squeeze more juice" out of each cohort than what is possible in a single communication round. Surprisingly, we find that this is indeed the case, and our approach leads to up to 74% reduction in the total communication cost needed to train a FL model in the cross-device setting. Our method is based on a novel variant of the stochastic proximal point method (SPPM-AS) which supports a large collection of client sampling procedures some of which lead to further gains when compared to classical client selection approaches.

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