Multi-Model Federated Learning with Provable Guarantees

• URL: http://arxiv.org/abs/2207.04330v3
• Date: Wed, 13 Jul 2022 08:09:13 GMT
• Title: Multi-Model Federated Learning with Provable Guarantees
• Authors: Neelkamal Bhuyan, Sharayu Moharir, Gauri Joshi
• Abstract summary: Federated Learning (FL) is a variant of distributed learning where devices collaborate to learn a model without sharing their data with the central server or each other. We refer to the process of multiple independent clients simultaneously in a federated setting using a common pool of clients as a multi-model edge FL.
• Score: 19.470024548995717
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is a variant of distributed learning where edge devices collaborate to learn a model without sharing their data with the central server or each other. We refer to the process of training multiple independent models simultaneously in a federated setting using a common pool of clients as multi-model FL. In this work, we propose two variants of the popular FedAvg algorithm for multi-model FL, with provable convergence guarantees. We further show that for the same amount of computation, multi-model FL can have better performance than training each model separately. We supplement our theoretical results with experiments in strongly convex, convex, and non-convex settings.

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