Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach

TL;DR

This paper introduces MU-SplitFed, a novel unbalanced update method for split federated learning that enhances resilience to stragglers by allowing multiple local updates, thereby improving convergence and efficiency.

Contribution

The paper proposes MU-SplitFed, a straggler-resilient algorithm for split federated learning that decouples training progress from straggler delays using an unbalanced update mechanism.

Findings

Achieves a convergence rate of O(√(d/τT)) for non-convex objectives.

Demonstrates a linear speedup in communication rounds with increased local updates.

Outperforms baseline methods in the presence of stragglers.

Abstract

Split Federated Learning (SFL) enables scalable training on edge devices by combining the parallelism of Federated Learning (FL) with the computational offloading of Split Learning (SL). Despite its great success, SFL suffers significantly from the well-known straggler issue in distributed learning systems. This problem is exacerbated by the dependency between Split Server and clients: the Split Server side model update relies on receiving activations from clients. Such synchronization requirement introduces significant time latency, making straggler a critical bottleneck to the scalability and efficiency of the system. To mitigate this problem, we propose MU-SplitFed, a straggler-resilient SFL algorithm in zeroth-order optimization that decouples training progress from straggler delays via a simple yet effective unbalanced update mechanism. By enabling the server to perform $\tau$ local updates per client round, MU-SplitFed achieves a convergence rate of $O(\sqrt{d/(\tau T)})$ for non-convex objectives, demonstrating a linear speedup of $\tau$ in communication rounds. Experiments demonstrate that MU-SplitFed consistently outperforms baseline methods with the presence of stragglers and effectively mitigates their impact through adaptive tuning of $\tau$. The code for this project is available at https://github.com/Johnny-Zip/MU-SplitFed.