FREB: Participant Selection in Federated Learning With Reputation Evaluation and Blockchain

Federated Learning (FL) offers a distributed machine learning framework that enables collaborative model training across multiple data sources without the need to share raw data, thereby preserving data privacy. This framework is particularly well-suited for cross-departmental and cross-enterprise intelligent decision-making in smart manufacturing. However, challenges remain in selecting reliable participants and ensuring the secure transmission of parameters to defend against potential attacks. Malicious participants may upload low-quality data or compromise data privacy during model aggregation. To address these issues, we propose the Federated Reputation Evaluation Blockchain (FREB), which integrates a reputation evaluation mechanism with blockchain technology. By leveraging blockchain, FL tasks are executed through trusted transactions, with smart contracts ensuring transparency and accountability. In contrast to traditional contribution evaluation methods, FREB employs a multi-weight subjective logic model combined with Shapley values to assess participant reliability. Reputation scores are calculated based on factors such as activity, model contribution, stability, and data quality, guiding the selection of participants. Additionally, a PoR-based model aggregation method is implemented, and noise is added to the model parameters to protect sensitive data from potential attacks. Experimental results on real-world datasets demonstrate that FREB effectively mitigates malicious node attacks and encourages high-quality participants, while maintaining model accuracy and data privacy.