Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6): 446-459.doi: 10.11896/jsjkx.250600089

• Information Security • Previous Articles    

Edge Federated Learning Privacy Protection Scheme Based on Multi-key Homomorphic Encryption

LI Ruirui1,2, GUO Rui1,2, ZHANG Yinghui1,2, LI Xuelei3, LIU Guangjun4   

  1. 1 School of Cyber Security,Xi'an University of Posts and Telecommunications,Xi'an 710121,China
    2 National Engineering Research Center for Secured Wireless,Xi'an University of Posts and Telecommunications,Xi'an 710121,China
    3 IEIT SYSTEMS(Beijing) Co.,Ltd.,Beijing 100089,China
    4 School of Information Engineering,Xi'an University,Xi'an 710065,China
  • Received:2025-06-12 Revised:2025-09-10 Online:2026-06-15 Published:2026-06-09
  • About author:LI Ruirui,born in 2001,postgraduate.Her main research interests include homomorphic encryption,and so on.
    GUO Rui,born in 1984,Ph.D,professor,is a member of CCF(No.E3529M).His main research interests include privacy preserving,blockchain,and so on.
  • Supported by:
    National Cryptologic Science Fundation of China(2025NCSF02037),National Natural Science Foundation of China(62072369),Beijing Nova Program(20230484455),Shaanxi Provincial Key Research and Development Program(2020ZDLGY08-04),Innovation Capacity Support Program of Shaanxi Province(2020KJXX-052),General Program of Natural Science Foundation of Shaanxi Province(2024JC-YBMS-545,2024JC-YBMS-557),Youth Innovation Team of Shaanxi Universities(23JP160,24JP180,25JP178) and Science and Technology Program of Xi'an(23KGDW0018-2023).

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Abstract: Federated learning allows users to collaboratively train a machine learning model by aggregating local model updates without sharing raw data.However,traditional federated learning faces issues such as reliance on a central server leading to single points of failure,privacy leakage,and communication bottlenecks.To address these problems,this paper proposes a decentralized multi-edge distributed federated learning privacy protection scheme.A local model aggregation mechanism based on Aggregation Multi-Key Cheon-Kim-Kim-Song(AMK-CKKS)is designed,it implements privacy protection for the raw data owned by data owners.Additionally,a distributed global model aggregation framework is constructed using the RingAllreduce algorithm,where edge servers replace the central server for global model aggregation,effectively reducing communication load and eliminating dependence on central nodes.Furthermore,the introduction of blockchain and the SG-PBFT consensus mechanism ensures that model update parameters are auditable and allows nodes to reach consensus quickly while ensuring the security of honest nodes during operation.Security analysis indicates that this scheme not only ensures the privacy of the model update parameters but also resists collusion attacks involving up tok<(n－2) participants.Moreover,compared to related schemes,the accuracy loss of the proposed model does not exceed 3%,and communication overhead is reduced by approximately 76%.

Key words: Federated learning, Privacy protection, Multi-key homomorphic encryption, Edge computing, Blockchain

CLC Number: 

  • TP309
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