mlsanityData to 14 Oct 2026

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems

arXiv 2506.08426 ↗First posted 10 Jun 2025cs.LG cs.AI cs.DCcited by 15

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Abstract (arXiv)

Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer-wise model partitioning. However, existing SFL approaches suffer significantly from the straggler effect due to the heterogeneous capabilities of edge devices. To address the fundamental challenge, we propose adaptively controlling batch sizes (BSs) and model splitting (MS) for edge devices to overcome resource heterogeneity. We first derive a tight convergence bound of SFL that quantifies the impact of varied BSs and MS on learning performance. Based on the convergence bound, we propose HASFL, a heterogeneity-aware SFL framework capable of adaptively controlling BS and MS to balance communication-computing latency and training convergence in heterogeneous edge networks. Extensive experiments with various datasets validate the effectiveness of HASFL and demonstrate its superiority over state-of-the-art benchmarks.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 37 of its 59 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

37 of 59 references.

References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 502
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  7. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  8. 2017Don't Decay the Learning Rate, Increase the Batch Sizecited by 320
  9. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  10. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  11. 2018Don't Use Large Mini-Batches, Use Local SGDcited by 202
  12. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
25 more
  1. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  2. 2019On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimizationcited by 180
  3. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  4. 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 105
  5. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  6. 2017On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimizationcited by 92
  7. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  8. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 90
  9. 2015Speeding Up Distributed Machine Learning Using Codescited by 76
  10. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  11. 2015Perturbed Iterate Analysis for Asynchronous Stochastic Optimizationcited by 67
  12. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  13. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  14. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  15. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  16. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  17. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 31
  18. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
  19. 2024Convergence Analysis of Split Federated Learning on Heterogeneous Datacited by 17
  20. 2019On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimizationcited by 15
  21. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
  22. 2023MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulationcited by 13
  23. 2022Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computingcited by 8
  24. 2023Time-sensitive Learning for Heterogeneous Federated Edge Intelligencecited by 8
  25. 2025Pipelining Split Learning in Multi-hop Edge Networkscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 12.

15 papers.

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  1. 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
  2. 2025LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systemscited by 8
  3. 2025A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observationcited by 3
  4. 2025Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computingcited by 1
  5. 2026ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learningcited by 1
  6. 2026SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compressioncited by 1
  7. 2026Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classificationcited by 1
  8. 2026SIDeR: Semantic Identity Decoupling for Unrestricted Face Privacycited by 0
  9. 2026Exploiting Adaptive Channel Pruning for Communication-Efficient Split Learningcited by 0
  10. 2026GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systemscited by 0
  11. 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregationcited by 0
  12. 2026TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectorscited by 0
3 more
  1. 2026SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoningcited by 0
  2. 2026Latency-Aware Client Assignment for Parallel Split Learning With Global Samplingcited by 0
  3. 2026Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimizationcited by 0