mlsanityData to 14 Oct 2026

Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryption

arXiv 2104.06824 ↗First posted 14 Apr 2021cs.CRcited by 48

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Abstract (arXiv)

With the advance of machine learning and the internet of things (IoT), security and privacy have become key concerns in mobile services and networks. Transferring data to a central unit violates privacy as well as protection of sensitive data while increasing bandwidth demands.Federated learning mitigates this need to transfer local data by sharing model updates only. However, data leakage still remains an issue. In this paper, we propose xMK-CKKS, a multi-key homomorphic encryption protocol to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, collaboration between all participating devices is required. This scheme prevents privacy leakage from publicly shared information in federated learning, and is robust to collusion between k<N-1 participating devices and the server. Our experimental evaluation demonstrates that the scheme preserves model accuracy against traditional federated learning as well as secure federated learning with homomorphic encryption (MK-CKKS, Paillier) and reduces computational cost compared to Paillier based federated learning. The average energy consumption is 2.4 Watts, so that it is suited to IoT scenarios.

Key facts. Cited by 48 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Apr 2021.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 11; 2024: 9; 2025: 9; 2026: 11.

48 papers.

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  1. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 84
  2. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  3. 2023FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning Systemcited by 38
  4. 2025Vision-Language Models for Edge Networks: A Comprehensive Surveycited by 29
  5. 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learningcited by 24
  6. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  7. 2022Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooringcited by 17
  8. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  9. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  10. 2023When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protectioncited by 7
  11. 2023Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregationcited by 6
  12. 2023FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Userscited by 3
36 more
  1. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  2. 2022Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networkscited by 2
  3. 2022Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learningcited by 2
  4. 2023Mitigating Cross-client GANs-based Attack in Federated Learningcited by 2
  5. 2024Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generatorcited by 2
  6. 2025A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networkscited by 2
  7. 2022Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryptioncited by 1
  8. 2023Federated Learning over Harmonized Data Siloscited by 1
  9. 2023Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attackscited by 1
  10. 2024Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systemscited by 1
  11. 2024Privacy-preserving gradient-based fair federated learningcited by 1
  12. 2024On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learningcited by 1
  13. 2025PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attackscited by 1
  14. 2025Hi-SAFE: Hierarchical Secure Aggregation for Lightweight Federated Learningcited by 1
  15. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1
  16. 2026CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learningcited by 1
  17. 2022Secure & Private Federated Neuroimagingcited by 0
  18. 2023SABLE: Secure And Byzantine robust LEarningcited by 0
  19. 2023On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networkscited by 0
  20. 2024A Multivocal Literature Review on Privacy and Fairness in Federated Learningcited by 0
  21. 2024Immersion and Invariance-based Coding for Privacy-Preserving Federated Learningcited by 0
  22. 2024Privacy-Preserving Federated Learning via Homomorphic Adversarial Networkscited by 0
  23. 2025Outsourced Privacy-Preserving Feature Selection Based on Fully Homomorphic Encryptioncited by 0
  24. 2025ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learningcited by 0
  25. 2025One-Shot Secure Aggregation: A Hybrid Cryptographic Protocol for Private Federated Learning in IoTcited by 0
  26. 2025Secure and Privacy-Preserving Federated Learning for Next-Generation Underground Mine Safetycited by 0
  27. 2026FedHypeVAE: Federated Learning with Hypernetwork Generated Conditional VAEs for Differentially Private Embedding Sharingcited by 0
  28. 2026Local Layer-wise Differential Privacy in Federated Learningcited by 0
  29. 2026Communication-efficient Federated Graph Classification via Generative Diffusion Modelingcited by 0
  30. 2026Agentic Federated Learning: The Future of Distributed Training Orchestrationcited by 0
  31. 2026Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryptioncited by 0
  32. 2026Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channelscited by 0
  33. 2026Privacy-Preserving and Verifiable Approximate Distributed Coded Computingcited by 0
  34. 2026PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learningcited by 0
  35. 2026Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregationcited by 0
  36. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0