Unbounded Gradients in Federated Leaning with Buffered Asynchronous Aggregation

• URL: http://arxiv.org/abs/2210.01161v1
• Date: Mon, 3 Oct 2022 18:20:48 GMT
• Title: Unbounded Gradients in Federated Leaning with Buffered Asynchronous Aggregation
• Authors: Mohammad Taha Toghani and C\'esar A. Uribe
• Abstract summary: The textitFedBuff algorithm allows asynchronous updates while preserving privacy via secure aggregation. This paper presents a theoretical analysis of the convergence rate of this algorithm when heterogeneity in data, batch size, and delay are considered.
• Score: 0.6526824510982799
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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