mlsanityData to 14 Oct 2026

Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning

arXiv 2502.05547 ↗First posted 8 Feb 2025cs.CR cs.AIcited by 3

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Abstract (arXiv)

Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mechanisms to protect data privacy and robust aggregation methods that withstand poisoning attacks. However, simultaneously addressing both concerns is challenging; secure aggregation facilitates poisoning attacks as most anomaly detection techniques require access to unencrypted local model updates, which are obscured by secure aggregation. Few recent efforts to simultaneously tackle both challenges offen depend on impractical assumption of non-colluding two-server setups that disrupt FL's topology, or three-party computation which introduces scalability issues, complicating deployment and application. To overcome this dilemma, this paper introduce a Dual Defense Federated learning (DDFed) framework. DDFed simultaneously boosts privacy protection and mitigates poisoning attacks, without introducing new participant roles or disrupting the existing FL topology. DDFed initially leverages cutting-edge fully homomorphic encryption (FHE) to securely aggregate model updates, without the impractical requirement for non-colluding two-server setups and ensures strong privacy protection. Additionally, we proposes a unique two-phase anomaly detection mechanism for encrypted model updates, featuring secure similarity computation and feedback-driven collaborative selection, with additional measures to prevent potential privacy breaches from Byzantine clients incorporated into the detection process. We conducted extensive experiments on various model poisoning attacks and FL scenarios, including both cross-device and cross-silo FL. Experiments on publicly available datasets demonstrate that DDFed successfully protects model privacy and effectively defends against model poisoning threats.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 8 Feb 2025.

Builds on

27 of 43 references.

References · 27

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2018How To Backdoor Federated Learningcited by 765
  6. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  7. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  8. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  9. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  10. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  11. 2019Robust Aggregation for Federated Learningcited by 317
  12. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
15 more
  1. 2019Can You Really Backdoor Federated Learning?cited by 255
  2. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  3. 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
  4. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  5. 2021TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryptioncited by 52
  6. 2019HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learningcited by 51
  7. 2020A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learningcited by 41
  8. 2022FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learningcited by 37
  9. 2021FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspectivecited by 36
  10. 2022DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Settingcited by 12
  11. 2021DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learningcited by 11
  12. 2023Secure Federated Learning against Model Poisoning Attacks via Client Filteringcited by 8
  13. 2020Mitigating Sybil Attacks on Differential Privacy based Federated Learningcited by 5
  14. 2021SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregationcited by 5
  15. 2022Towards a Defense Against Federated Backdoor Attacks Under Continuous Trainingcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learningcited by 1
  2. 2026DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Modelscited by 1
  3. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0