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  1. Home
  3. Towards Straggler-resilient Split Federated Learning: An Unbalanced Update Approach.
  1. Home
  3. Towards Straggler-resilient Split Federated Learning: An Unbalanced Update Approach.

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

Dandan Liang1, Jianing Zhang2, Evan Chen2

  • 1Rochester Institute of Technology, Rochester, New York.

Advances in Neural Information Processing Systems
|June 3, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Split Federated Learning (SFL) faces challenges from slow clients (stragglers). MU-SplitFed, a new algorithm, overcomes these delays by allowing more server updates, improving training efficiency and scalability in distributed systems.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Distributed Systems

Background:

  • Split Federated Learning (SFL) combines Federated Learning (FL) and Split Learning (SL) for scalable edge device training.
  • SFL is hindered by the 'straggler issue,' where slow clients create significant latency.
  • Client-server dependency in SFL exacerbates delays, bottlenecking system efficiency.

Purpose of the Study:

  • To propose a straggler-resilient Split Federated Learning algorithm.
  • To decouple training progress from straggler delays in SFL systems.
  • To enhance the scalability and efficiency of SFL by mitigating straggler impact.

Main Methods:

  • Introduced MU-SplitFed, a zeroth-order optimization algorithm for SFL.
  • Implemented an unbalanced update mechanism allowing the server to perform τ local updates per client round.
  • Analyzed convergence rate for non-convex objectives as 𝒪(√(d/(τT))) with linear speedup in τ communication rounds.

    • Main Results:

    • MU-SplitFed demonstrates resilience to straggler delays in SFL.
    • Achieved a convergence rate showing linear speedup with respect to the number of server updates (τ).
    • Experimental results confirm superior performance over baseline methods in the presence of stragglers.

    Conclusions:

    • MU-SplitFed effectively mitigates the straggler bottleneck in SFL.
    • The adaptive tuning of τ allows for effective management of straggler impact.
    • The proposed method enhances the practical applicability of SFL in real-world distributed systems.