AFLBCRS: Blockchain-Enabled Federated Learning with Ring Signatures

With the explosive development of machine learning and increased concern about data privacy, federated learning (FL) has emerged as a major area of study. Despite the benefits of FL, it deals with certain obstacles, including the risk of indirect data leaking via reverse engineering, the compromise of model architectural privacy, and the cost of connection and communication. Therefore, the proposed framework AFLBRS, or Adaptive Federated Learning with Blockchain and Ring Signatures, is an innovative framework that combines federated learning, blockchain technology, and ring signatures to enable collaborative and secure model training across decentralized networks while preserving data privacy. In AFLBRS, participants train local models using their private data and contribute updates to a shared model without disclosing raw data. Blockchain technology ensures the integrity and transparency of the process by securely recording and validating model updates. Ring signatures authenticate contributions while preserving participant anonymity. Key benefits of AFLBRS include privacy preservation, security, collaborative learning, and transparency. This framework is promising for applications in healthcare, finance, and other sensitive domains where data privacy and security are paramount. AFLBRS demonstrates competitive model accuracy compared to centralized approaches while effectively preserving data privacy and ensuring security through blockchain integration and ring signatures. The case study for AFLBCRS is a healthcare IoT setting using an ICU dataset, where multiple sites collaboratively trained a model to predict patient risk within 24 hours without sharing raw patient data. The results suggest that AFLBCRS is well-suited for compliance-focused environments because it keeps data local, protects participant identity, maintains an auditable (tamper-resistant) record of contributions, and ensures that only verified updates are accepted. When evaluated with a scoring method that prioritizes regulatory requirements alongside model usefulness and operational cost, AFLBCRS clearly outperformed a traditional centralized setup (0.898 vs. 0.343). The evaluation matrix for AFLBRS indicates promising results across key metrics such as model accuracy, privacy preservation, security, scalability, and usability.