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<p>To address the challenges of model parameter leakage, central server single-point failure, and insufficient auditability in federated learning (FL) for 6&#xa0;G edge scenarios, this paper proposes a blockchain-enabled privacy-preserving and auditable FL scheme (BPFL). BPFL replaces traditional centralized servers with blockchain to achieve distributed model storage and aggregation, eliminating single-point failure risks. It integrates optimized Paillier homomorphic encryption, single-mask protection, and Shamir threshold sharing to ensure encrypted uploads and secure mask cancelation of client models. A robust aggregation protocol supporting dynamic client exits and leader-failure switching enhances the system stability. Experimental results show BPFL maintains comparable model accuracy to state-of-the-art solutions with better computational efficiency and communication overhead, especially stronger robustness under high client dropout rates.</p>

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A blockchain-enabled privacy-preserving and auditable federated learning scheme for 6G edge intelligent networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Fu Zhang,
  • Furong Yi,
  • Jing Wu,
  • Zhaofeng Ma

摘要

To address the challenges of model parameter leakage, central server single-point failure, and insufficient auditability in federated learning (FL) for 6 G edge scenarios, this paper proposes a blockchain-enabled privacy-preserving and auditable FL scheme (BPFL). BPFL replaces traditional centralized servers with blockchain to achieve distributed model storage and aggregation, eliminating single-point failure risks. It integrates optimized Paillier homomorphic encryption, single-mask protection, and Shamir threshold sharing to ensure encrypted uploads and secure mask cancelation of client models. A robust aggregation protocol supporting dynamic client exits and leader-failure switching enhances the system stability. Experimental results show BPFL maintains comparable model accuracy to state-of-the-art solutions with better computational efficiency and communication overhead, especially stronger robustness under high client dropout rates.