mlsanityData to 7 Oct 2026

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

arXiv 2510.21155 ↗First posted 24 Oct 2025cs.DC cs.AI cs.LGcited by 7

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Abstract (arXiv)

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 τ local updates per client round, MU-SplitFed achieves a convergence rate of O(√d/(τ T)) for non-convex objectives, demonstrating a linear speedup of τ 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 τ. The code for this project is available at https://github.com/Johnny-Zip/MU-SplitFed.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 34 of its 48 references that are in the map. First posted on arXiv 24 Oct 2025.

Builds on

34 of 48 references.

References · 34

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22 more
  1. 2020A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learningcited by 160
  2. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  3. 2019Split Learning for collaborative deep learning in healthcarecited by 72
  4. 2020NoPeek: Information leakage reduction to share activations in distributed deep learningcited by 66
  5. 2020Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneitycited by 56
  6. 2021A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimizationcited by 46
  7. 2022ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learningcited by 36
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  11. 2024Convergence Analysis of Split Federated Learning on Heterogeneous Datacited by 17
  12. 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropoutcited by 16
  13. 2022FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clientscited by 14
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  16. 2021Communication and Computation Reduction for Split Learning using Asynchronous Trainingcited by 11
  17. 2023Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learningcited by 5
  18. 2023Convergence Analysis of Sequential Split Learning on Heterogeneous Datacited by 3
  19. 2024GAS: Generative Activation-Aided Asynchronous Split Federated Learningcited by 3
  20. 2022Enhancing Efficiency in Multidevice Federated Learning through Data Selectioncited by 2
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.

  1. 2025Quantized-Tinyllava: a new multimodal foundation model enables efficient split learningcited by 8
  2. 2026HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Trainingcited by 3
  3. 2026Self-Creating Random Walks for Decentralized Learning under Pac-Man Attackscited by 1
  4. 2026HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregationcited by 1
  5. 2026When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimationcited by 0
  6. 2026Latency-Aware Client Assignment for Parallel Split Learning With Global Samplingcited by 0
  7. 2026CoeF-SFL: Preserving Collaborative Server-Client Learning with Enhanced Communication Efficiencycited by 0