Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularization

Haemin Park, Diego Klabjan

Keywords: Federated Learning, Communication-Efficient Federated Learning, Low-Rank Nature, Cross-Device Federated Learning

TL;DR: We examine the rank structure in federated learning and introduce a new FL algorithm that uses low-rank updates, improving communication efficiency and scalability with a large number of clients.

Abstract:

Federated Learning (FL) faces significant challenges related to communication efficiency and heterogeneity. To address these issues, we explore the potential of using low-rank updates. Our theoretical analysis reveals that client's loss exhibits a higher rank structure (gradients span higher rank subspaces of Hessian) compared to the server's loss. Based on this insight, we hypothesize that constraining client-side optimization to a low-rank subspace could provide an implicit regularization effect. Consequently, we propose FedLoRU, a general low-rank update framework for FL. Our framework enforces low-rank client-side updates and accumulates these updates to form a higher-rank model. Additionally, variants of FedLoRU can adapt to environments with statistical and model heterogeneity by employing multiple or hierarchical low-rank updates. Experimental results demonstrate that FedLoRU performs comparably to full-rank algorithms and exhibits robustness to heterogeneous and large numbers of clients.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 8334