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FRIDA: Free-Rider Detection using Privacy Attacks

arXiv 2410.05020 ↗First posted 7 Oct 2024cs.LG cs.CRcited by 0

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Abstract (arXiv)

Federated learning is increasingly popular as it enables multiple parties with limited datasets and resources to train a machine learning model collaboratively. However, similar to other collaborative systems, federated learning is vulnerable to free-riders - participants who benefit from the global model without contributing. Free-riders compromise the integrity of the learning process and slow down the convergence of the global model, resulting in increased costs for honest participants. To address this challenge, we propose FRIDA: free-rider detection using privacy attacks. Instead of focusing on implicit effects of free-riding, FRIDA utilizes membership and property inference attacks to directly infer evidence of genuine client training. Our extensive evaluation demonstrates that FRIDA is effective across a wide range of scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 40 of its 70 references that are in the map. First posted on arXiv 7 Oct 2024.

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40 of 70 references.

References · 40

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  6. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  7. 2019Deep Leakage from Gradientscited by 1,021
  8. 2018How To Backdoor Federated Learningcited by 765
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  10. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 468
  11. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  12. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
28 more
  1. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  2. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  3. 2019Robust Aggregation for Federated Learningcited by 317
  4. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  5. 2019White-box vs Black-box: Bayes Optimal Strategies for Membership Inferencecited by 271
  6. 2018Mitigating Sybils in Federated Learning Poisoningcited by 161
  7. 2013Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifierscited by 130
  8. 2022The Shapley Value in Machine Learningcited by 98
  9. 2020Collaborative Fairness in Federated Learningcited by 95
  10. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  11. 2020A Distributional Framework for Data Valuationcited by 80
  12. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  13. 2023OpenDataVal: a Unified Benchmark for Data Valuationcited by 55
  14. 2019Free-riders in Federated Learning: Attacks and Defensescited by 49
  15. 2020A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learningcited by 41
  16. 2021Property Inference Attacks Against GANscited by 33
  17. 2019Eavesdrop the Composition Proportion of Training Labels in Federated Learningcited by 27
  18. 2021User-Level Label Leakage from Gradients in Federated Learningcited by 21
  19. 2021Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexitycited by 19
  20. 2022Shielding Federated Learning: Robust Aggregation with Adaptive Client Selectioncited by 18
  21. 2023FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregationcited by 16
  22. 2020Quality Inference in Federated Learning with Secure Aggregationcited by 15
  23. 2020An Exploratory Analysis on Users' Contributions in Federated Learningcited by 15
  24. 2024Unveiling the Unseen: Exploring Whitebox Membership Inference through the Lens of Explainabilitycited by 6
  25. 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
  26. 2024Decaf: Data Distribution Decompose Attack against Federated Learningcited by 4
  27. 2022Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequencycited by 3
  28. 2022PASS: A Parameter Audit-based Secure and Fair Federated Learning Scheme against Free-Rider Attackcited by 2

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