Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation

Mohammad Taha Toghani; César A. Uribe

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arXiv · 2210.01161

Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation

Mohammad Taha Toghani·César A. Uribe

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Abstract

Synchronous updates may compromise the efficiency of cross-device federated learning once the number of active clients increases. The \textit{FedBuff} algorithm (Nguyen et al., 2022) alleviates this problem by allowing asynchronous updates (staleness), which enhances the scalability of training while preserving privacy via secure aggregation. We revisit the \textit{FedBuff} algorithm for asynchronous federated learning and extend the existing analysis by removing the boundedness assumptions from the gradient norm. This paper presents a theoretical analysis of the convergence rate of this algorithm when heterogeneity in data, batch size, and delay are considered.

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BibTeX RIS

Mohammad Taha Toghani, César A. Uribe. 2022-10-03. Unbounded Gradients in Federated Learning with Buffered Asynchronous Aggregation. https://arxiv.org/abs/2210.01161

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