mlsanityData to 7 Oct 2026

FedDefender: Client-Side Attack-Tolerant Federated Learning

arXiv 2307.09048 ↗First posted 18 Jul 2023cs.CR cs.AIcited by 12

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Abstract (arXiv)

Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning attacks, where malicious clients interfere with the training process. Previous defense mechanisms have focused on the server-side by using careful model aggregation, but this may not be effective when the data is not identically distributed or when attackers can access the information of benign clients. In this paper, we propose a new defense mechanism that focuses on the client-side, called FedDefender, to help benign clients train robust local models and avoid the adverse impact of malicious model updates from attackers, even when a server-side defense cannot identify or remove adversaries. Our method consists of two main components: (1) attack-tolerant local meta update and (2) attack-tolerant global knowledge distillation. These components are used to find noise-resilient model parameters while accurately extracting knowledge from a potentially corrupted global model. Our client-side defense strategy has a flexible structure and can work in conjunction with any existing server-side strategies. Evaluations of real-world scenarios across multiple datasets show that the proposed method enhances the robustness of federated learning against model poisoning attacks.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 37 of its 52 references that are in the map. First posted on arXiv 18 Jul 2023.

Builds on

37 of 52 references.

References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2017Visualizing the Loss Landscape of Neural Netscited by 1,126
  7. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
  8. 2019Towards Federated Learning at Scale: System Designcited by 826
  9. 2018LEAF: A Benchmark for Federated Settingscited by 780
  10. 2021Model-Contrastive Federated Learningcited by 762
  11. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  12. 2020The Future of Digital Health with Federated Learningcited by 625
25 more
  1. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
  2. 2019Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillationcited by 491
  3. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  4. 2019Robust Aggregation for Federated Learningcited by 314
  5. 2021A Field Guide to Federated Optimizationcited by 312
  6. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 285
  7. 2019Can You Really Backdoor Federated Learning?cited by 250
  8. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 185
  9. 2020Threats to Federated Learning: A Surveycited by 171
  10. 2018Learning to Learn from Noisy Labeled Datacited by 160
  11. 2017Revisiting Distributed Synchronous SGDcited by 142
  12. 2021Deep Learning Through the Lens of Example Difficultycited by 139
  13. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 125
  14. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 116
  15. 2018Generalized Byzantine-tolerant SGDcited by 94
  16. 2021On the geometry of generalization and memorization in deep neural networkscited by 71
  17. 2020Improving Unsupervised Image Clustering With Robust Learningcited by 51
  18. 2020Performance Optimization for Federated Person Re-identification via Benchmark Analysiscited by 50
  19. 2019Attack-Resistant Federated Learning with Residual-based Reweightingcited by 40
  20. 2022FedX: Unsupervised Federated Learning with Cross Knowledge Distillationcited by 40
  21. 2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 35
  22. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
  23. 2022FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Samplingcited by 21
  24. 2022Federated Learning Attacks and Defenses: A Surveycited by 11
  25. 2022Knowledge Sharing via Domain Adaptation in Customs Fraud Detectioncited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 2; 2026: 2.

12 papers.

  1. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 124
  2. 2024FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Modelscited by 26
  3. 2024Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Modelscited by 18
  4. 2024Federated Distillation: A Surveycited by 14
  5. 2023Towards Attack-tolerant Federated Learning via Critical Parameter Analysiscited by 7
  6. 2024Poisoning with A Pill: Circumventing Detection in Federated Learningcited by 2
  7. 2024FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learningcited by 0
  8. 2024Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learningcited by 0
  9. 2025An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosiscited by 0
  10. 2025Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networkscited by 0
  11. 2026FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matchingcited by 0
  12. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0