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FedCAP: Robust Federated Learning via Customized Aggregation and Personalization

arXiv 2410.13083 ↗First posted 16 Oct 2024cs.LG cs.AI cs.CRcited by 0

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Abstract (arXiv)

Federated learning (FL), an emerging distributed machine learning paradigm, has been applied to various privacy-preserving scenarios. However, due to its distributed nature, FL faces two key issues: the non-independent and identical distribution (non-IID) of user data and vulnerability to Byzantine threats. To address these challenges, in this paper, we propose FedCAP, a robust FL framework against both data heterogeneity and Byzantine attacks. The core of FedCAP is a model update calibration mechanism to help a server capture the differences in the direction and magnitude of model updates among clients. Furthermore, we design a customized model aggregation rule that facilitates collaborative training among similar clients while accelerating the model deterioration of malicious clients. With a Euclidean norm-based anomaly detection mechanism, the server can quickly identify and permanently remove malicious clients. Moreover, the impact of data heterogeneity and Byzantine attacks can be further mitigated through personalization on the client side. We conduct extensive experiments, comparing multiple state-of-the-art baselines, to demonstrate that FedCAP performs well in several non-IID settings and shows strong robustness under a series of poisoning attacks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 45 references that are in the map. First posted on arXiv 16 Oct 2024.

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References · 31

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
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  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
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  12. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
19 more
  1. 2019Robust Aggregation for Federated Learningcited by 314
  2. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 285
  3. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  4. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  5. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  6. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  7. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163
  8. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 136
  9. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 125
  10. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 119
  11. 2022The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retrainingcited by 109
  12. 2022Tackling Data Heterogeneity in Federated Learning with Class Prototypescited by 66
  13. 2020Byzantine-Resilient Non-Convex Stochastic Gradient Descentcited by 56
  14. 2022FedAvg with Fine Tuning: Local Updates Lead to Representation Learningcited by 55
  15. 2023An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learningcited by 32
  16. 2022AFLGuard: Byzantine-robust Asynchronous Federated Learningcited by 29
  17. 2021Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filteringcited by 28
  18. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 19
  19. 2023Byzantine-Robust Learning on Heterogeneous Data via Gradient Splittingcited by 13

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