mlsanityData to 7 Oct 2026

Tackling Selfish Clients in Federated Learning

arXiv 2407.15402 ↗First posted 22 Jul 2024cs.LG cs.AI cs.DCcited by 3

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Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL is deployed into the wild, some intelligent clients can deliberately deviate from the standard training process to make the global model inclined toward their local model, thereby prioritizing their local data distribution. We refer to this novel category of misbehaving clients as selfish. In this paper, we propose a Robust aggregation strategy for FL server to mitigate the effect of Selfishness (in short RFL-Self). RFL-Self incorporates an innovative method to recover (or estimate) the true updates of selfish clients from the received ones, leveraging robust statistics (median of norms) of the updates at every round. By including the recovered updates in aggregation, our strategy offers strong robustness against selfishness. Our experimental results, obtained on MNIST and CIFAR-10 datasets, demonstrate that just 2% of clients behaving selfishly can decrease the accuracy by up to 36%, and RFL-Self can mitigate that effect without degrading the global model performance.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 35 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

18 of 35 references.

References · 18

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2018How To Backdoor Federated Learningcited by 745
  3. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  4. 2020The Future of Digital Health with Federated Learningcited by 625
  5. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  6. 2021Towards Personalized Federated Learningcited by 462
  7. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  8. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  9. 2019Robust Aggregation for Federated Learningcited by 314
  10. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  11. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 119
  12. 2021Provably Secure Federated Learning against Malicious Clientscited by 60
6 more
  1. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  2. 2020Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneitycited by 56
  3. 2019Abnormal Client Behavior Detection in Federated Learningcited by 39
  4. 2020Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directionscited by 25
  5. 2022Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learningcited by 7
  6. 2022Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networkscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Can Quantum Federated Learning Withstand Circuit-Level Backdoors?cited by 0
  2. 2026Adaptive Bayesian Partner Selection for Federated Clinical Centerscited by 0
  3. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0