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<p>Traditional synchronous Federated Learning (FL) is subject to the waiting latency inherent to synchronization mechanisms. Consequently, its convergence rate is constrained by straggler nodes within heterogeneous environments. Asynchronous Federated Learning (AFL) improves execution efficiency by removing global synchronization barriers. However, when integrated with blockchain for decentralized deployment, it still encounters challenges such as on-chain storage overhead arising from model parameters, convergence perturbations induced by stale gradients, and Byzantine security threats. To this end, this paper proposes BCAFL, a decentralized blockchain framework tailored for semi-asynchronous federated learning. BCAFL utilizes the InterPlanetary File System (IPFS) to implement off-chain storage for global model parameters. By integrating Model-Agnostic Meta-Learning (MAML) and PowerSGD, the framework enhances the model’s local adaptation capability on non-IID data while concurrently reducing communication overhead. To safeguard model security and convergence stability in asynchronous environments, this study develops a Mutual Information and Delay-Aware (MIDA) dynamic aggregation mechanism. This mechanism leverages Mutual Information (MI) to perform model verification for defense against poisoning attacks, while simultaneously modulating aggregation weights via a dynamic aggregation factor to effectively mitigate model oscillations inherent in asynchronous convergence. Additionally, this study develops a dynamic stake-based Verifiable Random Function (VRF) committee consensus mechanism. By quantifying election weights based on node contributions, this approach enhances consensus efficiency and resistance to Sybil attacks. Simulation results demonstrate that, compared with various existing baseline schemes, BCAFL maintains the convergence accuracy of the global model while reducing communication overhead. It effectively suppresses convergence oscillations caused by asynchronous delays and defends against poisoning and Byzantine attacks. Furthermore, when the network scale is expanded to 300 nodes, the consensus latency does not show a significant increase.</p>

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BCAFL: a secure and efficient blockchain framework for asynchronous federated learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

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  • Jian Yun,
  • Tao Liu

摘要

Traditional synchronous Federated Learning (FL) is subject to the waiting latency inherent to synchronization mechanisms. Consequently, its convergence rate is constrained by straggler nodes within heterogeneous environments. Asynchronous Federated Learning (AFL) improves execution efficiency by removing global synchronization barriers. However, when integrated with blockchain for decentralized deployment, it still encounters challenges such as on-chain storage overhead arising from model parameters, convergence perturbations induced by stale gradients, and Byzantine security threats. To this end, this paper proposes BCAFL, a decentralized blockchain framework tailored for semi-asynchronous federated learning. BCAFL utilizes the InterPlanetary File System (IPFS) to implement off-chain storage for global model parameters. By integrating Model-Agnostic Meta-Learning (MAML) and PowerSGD, the framework enhances the model’s local adaptation capability on non-IID data while concurrently reducing communication overhead. To safeguard model security and convergence stability in asynchronous environments, this study develops a Mutual Information and Delay-Aware (MIDA) dynamic aggregation mechanism. This mechanism leverages Mutual Information (MI) to perform model verification for defense against poisoning attacks, while simultaneously modulating aggregation weights via a dynamic aggregation factor to effectively mitigate model oscillations inherent in asynchronous convergence. Additionally, this study develops a dynamic stake-based Verifiable Random Function (VRF) committee consensus mechanism. By quantifying election weights based on node contributions, this approach enhances consensus efficiency and resistance to Sybil attacks. Simulation results demonstrate that, compared with various existing baseline schemes, BCAFL maintains the convergence accuracy of the global model while reducing communication overhead. It effectively suppresses convergence oscillations caused by asynchronous delays and defends against poisoning and Byzantine attacks. Furthermore, when the network scale is expanded to 300 nodes, the consensus latency does not show a significant increase.