mlsanityData to 14 Oct 2026

Hierarchical Split Federated Learning: Convergence Analysis and System Optimization

arXiv 2412.07197 ↗First posted 10 Dec 2024cs.LG cs.AI cs.DCcited by 38

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Abstract (arXiv)

As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated learning (SFL) has emerged as an FL framework with reduced workload on edge devices via model splitting; it has received extensive attention from the research community in recent years. Nevertheless, most prior works on SFL focus only on a two-tier architecture without harnessing multi-tier cloudedge computing resources. In this paper, we intend to analyze and optimize the learning performance of SFL under multi-tier systems. Specifically, we propose the hierarchical SFL (HSFL) framework and derive its convergence bound. Based on the theoretical results, we formulate a joint optimization problem for model splitting (MS) and model aggregation (MA). To solve this rather hard problem, we then decompose it into MS and MA subproblems that can be solved via an iterative descending algorithm. Simulation results demonstrate that the tailored algorithm can effectively optimize MS and MA for SFL within virtually any multi-tier system.

Key facts. Cited by 38 papers in the map (data through 14 Oct 2026). Builds on 37 of its 63 references that are in the map. First posted on arXiv 10 Dec 2024.

Builds on

37 of 63 references.

References · 37

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  4. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 502
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  6. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  7. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  8. 2018Don't Use Large Mini-Batches, Use Local SGDcited by 202
  9. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  10. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  11. 2019On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimizationcited by 180
  12. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
25 more
  1. 2017On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimizationcited by 92
  2. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  3. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 90
  4. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  5. 2023Split Learning in 6G Edge Networkscited by 68
  6. 2015Perturbed Iterate Analysis for Asynchronous Stochastic Optimizationcited by 67
  7. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  8. 2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 62
  9. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  10. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  11. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  12. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  13. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
  14. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  15. 2022A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networkscited by 22
  16. 2024SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Frameworkcited by 21
  17. 2024Convergence Analysis of Split Federated Learning on Heterogeneous Datacited by 17
  18. 2024IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformercited by 16
  19. 2019On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimizationcited by 15
  20. 2023Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshnesscited by 15
  21. 2023Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaborationcited by 10
  22. 2023Federated Learning While Providing Model as a Service: Joint Training and Inference Optimizationcited by 8
  23. 2024Workflow Optimization for Parallel Split Learningcited by 8
  24. 2023Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networkscited by 2
  25. 2024Expediting In-Network Federated Learning by Voting-Based Consensus Model Compressioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 19; 2026: 18.

38 papers.

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  1. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 31
  2. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
  3. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  4. 2026Multimodal Multi-Agent Empowered Legal Judgment Predictioncited by 14
  5. 2024CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perceptioncited by 13
  6. 2026HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Expertscited by 11
  7. 2025PRISM: Privacy-Aware Routing for Adaptive Cloud-Edge LLM Inference via Semantic Sketch Collaborationcited by 10
  8. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 8
  9. 2025LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systemscited by 8
  10. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
  11. 2025Rethinking Membership Inference Attacks Against Transfer Learningcited by 6
  12. 2025SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compressioncited by 6
26 more
  1. 2025LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learningcited by 6
  2. 2026Conflict-Aware Client Selection for Multi-Server Federated Learningcited by 5
  3. 2025Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generationcited by 3
  4. 2025Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edgecited by 2
  5. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 2
  6. 2026Lean Clients, Full Accuracy: Hybrid Zeroth- and First-Order Split Federated Learningcited by 2
  7. 2025Secure Resource Allocation via Constrained Deep Reinforcement Learningcited by 1
  8. 2025SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learningcited by 1
  9. 2026ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learningcited by 1
  10. 2026SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compressioncited by 1
  11. 2026Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classificationcited by 1
  12. 2025DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protectioncited by 0
  13. 2025Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directionscited by 0
  14. 2025Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimizationcited by 0
  15. 2025Parallax: Runtime Parallelization for Operator Fallbacks in Heterogeneous Edge Systemscited by 0
  16. 2026Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Networkcited by 0
  17. 2026Makespan Minimization in Split Learning: From Theory to Practicecited by 0
  18. 2026Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Trainingcited by 0
  19. 2026Exploiting Adaptive Channel Pruning for Communication-Efficient Split Learningcited by 0
  20. 2026GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systemscited by 0
  21. 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregationcited by 0
  22. 2026Fairness-Aware Federated Learning with Trajectory Shapley Valuecited by 0
  23. 2026Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Drivingcited by 0
  24. 2026Efficient Resource Optimization for Split Federated Learningcited by 0
  25. 2026SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoningcited by 0
  26. 2026Importance-Aware Feature Sparsification for Wireless Split Learningcited by 0