Secure and Dynamic Node Selection in Federated Learning: A Reputation-Based Approach with Blockchain

With the advancement of Artificial Intelligence and big data, federated learning (FL) has demonstrated significant application value. However, traditional aggregation node selection methods in FL lack the ability to adjust dynamically. Furthermore, node reputation scores are susceptible to external interference, affecting training performance, slowing model convergence, and weakening the system’s ability to handle complex environments and risks. Hence, this paper proposes a dynamic aggregation node selection scheme implemented using reputation scoring. To address the challenges of dynamic adjustment and comprehensive evaluation in node selection, this method incorporates six evaluation dimensions, namely, direct and objective reputation scoring, enabling dynamic and optimized node selection. To enhance system security and stability, cryptographic algorithms, signature verification techniques, and blockchain technologies are integrated to ensure data privacy and system integrity. The security analysis demonstrates that the proposed scheme enables the FL system to operate efficiently and securely, effectively resists malicious nodes, and improves the reliability of the system.