mlsanityData to 7 Oct 2026

Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning

arXiv 2209.02428 ↗First posted 2 Sep 2022cs.LG cs.IT math.ITcited by 11

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Abstract (arXiv)

As an edge intelligence algorithm for multi-device collaborative training, federated learning (FL) can reduce the communication burden but increase the computing load of wireless devices. In contrast, split learning (SL) can reduce the computing load of devices by using model splitting and assignment, but increase the communication burden to transmit intermediate results. In this paper, to exploit the advantages of FL and SL, we propose a hybrid federated split learning (HFSL) framework in wireless networks, which combines the multi-worker parallel update of FL and flexible splitting of SL. To reduce the computational idleness in model splitting, we design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed. Aiming to obtain the trade-off between the training time and energy consumption, we optimize the splitting decision, the bandwidth and computing resource allocation. The optimization problem is multi-objective, and we thus propose a predictive generative adversarial network (GAN)-powered multi-objective optimization algorithm to obtain the Pareto front of the problem. Experimental results show that the proposed algorithm outperforms others in finding Pareto optimal solutions, and the solutions of the proposed HFSL dominate the solution of FL.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 26 of its 66 references that are in the map. First posted on arXiv 2 Sep 2022.

Builds on

26 of 66 references.

References · 26

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2017Improved Training of Wasserstein GANscited by 2,223
  5. 2019Searching for MobileNetV3cited by 1,984
  6. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  7. 2019Why gradient clipping accelerates training: A theoretical justification for adaptivitycited by 335
  8. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  9. 2018Distributed learning of deep neural network over multiple agentscited by 277
  10. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  11. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
  12. 2019Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computingcited by 150
14 more
  1. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 143
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  3. 2019Convergence of Edge Computing and Deep Learning: A Comprehensive Surveycited by 120
  4. 2018Deep Learning in Mobile and Wireless Networking: A Surveycited by 117
  5. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 94
  6. 2019Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligencecited by 75
  7. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  8. 2019Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?cited by 38
  9. 2021Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Thingscited by 20
  10. 2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
  11. 2018Convergence guarantees for a class of non-convex and non-smooth optimization problemscited by 15
  12. 2019Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)cited by 14
  13. 2021Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networkscited by 7
  14. 2021DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Schedulingcited by 1

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Cited by year

Citing papers per year: 2023: 3; 2024: 6; 2025: 2.

11 papers.

  1. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  2. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
  3. 2024WDMoE: Wireless Distributed Mixture of Experts for Large Language Modelscited by 12
  4. 2024Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networkscited by 6
  5. 2023Probably Approximately Correct Federated Learningcited by 4
  6. 2024CollaFuse: Collaborative Diffusion Modelscited by 4
  7. 2024FedsLLM: Federated Split Learning for Large Language Models over Communication Networkscited by 3
  8. 2025Multi-objective methods in Federated Learning: A survey and taxonomycited by 3
  9. 2024Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learningcited by 2
  10. 2024CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AIcited by 0
  11. 2025Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimizationcited by 0