mlsanityData to 14 Oct 2026

Attack-Resistant Federated Learning with Residual-based Reweighting

arXiv 1912.11464 ↗First posted 24 Dec 2019cs.LG stat.MLcited by 40

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Abstract (arXiv)

Federated learning has a variety of applications in multiple domains by utilizing private training data stored on different devices. However, the aggregation process in federated learning is highly vulnerable to adversarial attacks so that the global model may behave abnormally under attacks. To tackle this challenge, we present a novel aggregation algorithm with residual-based reweighting to defend federated learning. Our aggregation algorithm combines repeated median regression with the reweighting scheme in iteratively reweighted least squares. Our experiments show that our aggregation algorithm outperforms other alternative algorithms in the presence of label-flipping and backdoor attacks. We also provide theoretical analysis for our aggregation algorithm.

Key facts. Cited by 40 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Dec 2019.

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Citing papers per year: 2021: 10; 2022: 12; 2023: 9; 2024: 5; 2025: 3; 2026: 1.

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  1. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 118
  2. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
  3. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 84
  4. 2022FedCorr: Multi-Stage Federated Learning for Label Noise Correctioncited by 61
  5. 2024Byzantine-Robust Decentralized Federated Learningcited by 48
  6. 2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 36
  7. 2022Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guaranteescited by 30
  8. 2023Multi-metrics adaptively identifies backdoors in Federated learningcited by 23
  9. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 21
  10. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 18
  11. 2024Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Modelscited by 18
  12. 2021Robust Federated Learning with Attack-Adaptive Aggregationcited by 17
28 more
  1. 2023FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labelscited by 13
  2. 2022Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attackscited by 12
  3. 2023FedDefender: Client-Side Attack-Tolerant Federated Learningcited by 12
  4. 2021Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Designcited by 9
  5. 2021Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learningcited by 9
  6. 2022Suppressing Poisoning Attacks on Federated Learning for Medical Imagingcited by 8
  7. 2022Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregationcited by 8
  8. 2022A Secure Federated Learning Framework for Residential Short Term Load Forecastingcited by 7
  9. 2023Towards Attack-tolerant Federated Learning via Critical Parameter Analysiscited by 7
  10. 2022Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbationscited by 5
  11. 2021LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learningcited by 4
  12. 2021Federated Learning: Issues in Medical Applicationcited by 4
  13. 2022Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanismscited by 4
  14. 2023Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learningcited by 4
  15. 2025Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspectivecited by 4
  16. 2021Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehiclescited by 3
  17. 2022Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimizationcited by 3
  18. 2021DistFL: Distribution-aware Federated Learning for Mobile Scenarioscited by 2
  19. 2023Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verificationcited by 2
  20. 2022Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustnesscited by 1
  21. 2023Harnessing the Power of Federated Learning in Federated Contextual Banditscited by 1
  22. 2024Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labelscited by 1
  23. 2025Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMscited by 1
  24. 2025GShield: Mitigating Poisoning Attacks in Federated Learningcited by 1
  25. 2026Safe-FedLLM: Delving into the Safety of Federated Large Language Modelscited by 1
  26. 2022Linear Scalarization for Byzantine-robust learning on non-IID datacited by 0
  27. 2024FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learningcited by 0
  28. 2024FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learningcited by 0