mlsanityData to 14 Oct 2026

Privacy and Efficiency of Communications in Federated Split Learning

arXiv 2301.01824 ↗First posted 4 Jan 2023cs.LG cs.CR cs.DCcited by 21

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Abstract (arXiv)

Everyday, large amounts of sensitive data is distributed across mobile phones, wearable devices, and other sensors. Traditionally, these enormous datasets have been processed on a single system, with complex models being trained to make valuable predictions. Distributed machine learning techniques such as Federated and Split Learning have recently been developed to protect user data and privacy better while ensuring high performance. Both of these distributed learning architectures have advantages and disadvantages. In this paper, we examine these tradeoffs and suggest a new hybrid Federated Split Learning architecture that combines the efficiency and privacy benefits of both. Our evaluation demonstrates how our hybrid Federated Split Learning approach can lower the amount of processing power required by each client running a distributed learning system, reduce training and inference time while keeping a similar accuracy. We also discuss the resiliency of our approach to deep learning privacy inference attacks and compare our solution to other recently proposed benchmarks.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 27 of its 59 references that are in the map. First posted on arXiv 4 Jan 2023.

Builds on

27 of 59 references.

References · 27

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  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. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2016Deep Learning with Differential Privacycited by 2,728
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  7. 2016Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimizationcited by 699
  8. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  9. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 527
  10. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 383
  11. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  12. 2018Distributed learning of deep neural network over multiple agentscited by 279
15 more
  1. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 118
  2. 2021Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challengescited by 115
  3. 2020Predicting Neural Network Accuracy from Weightscited by 110
  4. 2020FedSplit: An algorithmic framework for fast federated optimizationcited by 108
  5. 2018A generic framework for privacy preserving deep learningcited by 103
  6. 2018Federated Learning for Keyword Spottingcited by 95
  7. 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
  8. 2019BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Servicescited by 68
  9. 2020NoPeek: Information leakage reduction to share activations in distributed deep learningcited by 66
  10. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  11. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  12. 2022ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learningcited by 36
  13. 2020Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Managementcited by 26
  14. 2020FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networkscited by 23
  15. 2022A Comprehensive Survey on Federated Learning: Concept and Applicationscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 3; 2025: 9; 2026: 4.

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  1. 2023An Overview on Generative AI at Scale with Edge-Cloud Computingcited by 9
  2. 2023Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learningcited by 9
  3. 2024Workflow Optimization for Parallel Split Learningcited by 8
  4. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  5. 2024The Impact of Cut Layer Selection in Split Federated Learningcited by 6
  6. 2023Split Unlearningcited by 4
  7. 2023Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approachcited by 3
  8. 2025Federated Split Learning with Improved Communication and Storage Efficiencycited by 3
  9. 2025CycleSL: Server-Client Cyclical Update Driven Scalable Split Learningcited by 1
  10. 2026Guarding the Middle: Protecting Intermediate Representations in Federated Split Learningcited by 1
  11. 2026Privacy-Preserving Split Learning for Federated LLM Fine-Tuningcited by 1
  12. 2023When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Frameworkcited by 0
9 more
  1. 2025PEEL the Layers and Find Yourself: Revisiting Inference-time Data Leakage for Residual Neural Networkscited by 0
  2. 2025FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learningcited by 0
  3. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
  4. 2025ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculturecited by 0
  5. 2025HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabriccited by 0
  6. 2025Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participationcited by 0
  7. 2025Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directionscited by 0
  8. 2026Makespan Minimization in Split Learning: From Theory to Practicecited by 0
  9. 2026Federated Learning for LLMs over Mobile Networks: Issues and Solutions in the RAN Transportcited by 0