mlsanityData to 7 Oct 2026

ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning

arXiv 2205.04007 ↗First posted 9 May 2022cs.LGcited by 36

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Abstract (arXiv)

This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., feature map), instead of raw data, to a central server. While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server. Existing works on protecting SFL only consider inference and do not handle attacks during training. So we propose ResSFL, a Split Federated Learning Framework that is designed to be MI-resistant during training. It is based on deriving a resistant feature extractor via attacker-aware training, and using this extractor to initialize the client-side model prior to standard SFL training. Such a method helps in reducing the computational complexity due to use of strong inversion model in client-side adversarial training as well as vulnerability of attacks launched in early training epochs. On CIFAR-100 dataset, our proposed framework successfully mitigates MI attack on a VGG-11 model with a high reconstruction Mean-Square-Error of 0.050 compared to 0.005 obtained by the baseline system. The framework achieves 67.5% accuracy (only 1% accuracy drop) with very low computation overhead. Code is released at: https://github.com/zlijingtao/ResSFL.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 9 May 2022.

Builds on

14 of 27 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2022Generative Adversarial Networkscited by 5,603
  5. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  6. 2018Distributed learning of deep neural network over multiple agentscited by 277
  7. 2015Inverting Visual Representations with Convolutional Networkscited by 168
  8. 2018Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attackcited by 123
  9. 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 104
  10. 2019BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Servicescited by 67
  11. 2020NoPeek: Information leakage reduction to share activations in distributed deep learningcited by 66
  12. 2020Improving Robustness to Model Inversion Attacks via Mutual Information Regularizationcited by 49
2 more
  1. 2020Defending Model Inversion and Membership Inference Attacks via Prediction Purificationcited by 32
  2. 2021Practical Defences Against Model Inversion Attacks for Split Neural Networkscited by 29

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 9; 2024: 12; 2025: 10; 2026: 3.

36 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2024Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defensescited by 30
  3. 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 20
  4. 2023On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlookcited by 15
  5. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  6. 2024Trustworthy Distributed AI Systems: Robustness, Privacy, and Governancecited by 12
  7. 2023Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Informationcited by 11
  8. 2023Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attackscited by 8
  9. 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learningcited by 8
  10. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  11. 2024A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspectivecited by 7
  12. 2025Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approachcited by 7
24 more
  1. 2024Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learningcited by 6
  2. 2024The Impact of Cut Layer Selection in Split Federated Learningcited by 6
  3. 2025Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systemscited by 6
  4. 2023Model Extraction Attacks on Split Federated Learningcited by 5
  5. 2024MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defensecited by 5
  6. 2022SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networkscited by 4
  7. 2023The Model Inversion Eavesdropping Attack in Semantic Communication Systemscited by 3
  8. 2024GAS: Generative Activation-Aided Asynchronous Split Federated Learningcited by 3
  9. 2024FedBaF: Federated Learning Aggregation Biased by a Foundation Modelcited by 3
  10. 2025From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learningcited by 3
  11. 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inferencecited by 2
  12. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 2
  13. 2024Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemblecited by 1
  14. 2024Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learningcited by 1
  15. 2025SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perceptioncited by 1
  16. 2025FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retrainingcited by 1
  17. 2025A Taxonomy of Attacks and Defenses in Split Learningcited by 1
  18. 2025Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharingcited by 1
  19. 2025Oops!... They Stole it Again: Attacks on Split Learningcited by 1
  20. 2025Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challengescited by 0
  21. 2025Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directionscited by 0
  22. 2026CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learningcited by 0
  23. 2026A Privacy Study of Sparse Collaborative Inferencecited by 0
  24. 2026Spectrum-Aware Bounds on Invertibility for Privacy-Enhancing Instance Encodingcited by 0