Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting

Published: 16 Jan 2024, Last Modified: 13 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: federated domain adaptation, federated learning, domain adaptation, machine learning
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TL;DR: We address the problem of Federated Domain Adaptation in a principled way through investigating server aggregation rules, yielding both theoretical and practical contributions.
Abstract: Federated Domain Adaptation (FDA) describes the federated learning (FL) setting where source clients and a server work collaboratively to improve the performance of a target client where limited data is available. The domain shift between the source and target domains, coupled with limited data of the target client, makes FDA a challenging problem, e.g., common techniques such as federated averaging and fine-tuning fail due to domain shift and data scarcity. To theoretically understand the problem, we introduce new metrics that characterize the FDA setting and a theoretical framework with novel theorems for analyzing the performance of server aggregation rules. Further, we propose a novel lightweight aggregation rule, Federated Gradient Projection ($\texttt{FedGP}$), which significantly improves the target performance with domain shift and data scarcity. Moreover, our theory suggests an $\textit{auto-weighting scheme}$ that finds the optimal combinations of the source and target gradients. This scheme improves both $\texttt{FedGP}$ and a simpler heuristic aggregation rule. Extensive experiments verify the theoretical insights and illustrate the effectiveness of the proposed methods in practice.
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Primary Area: general machine learning (i.e., none of the above)
Submission Number: 2080