Federated Learning Meets Blockchain: A Kafka-ML Integration for reliable model training using data streams - Martín-Fernández, Cristian; Chaves, Antonio; Díaz-Rodríguez, Manuel; Shahid, Adnan; SKIm, Kwang Soon (IEEE, 2024)
Machine learning data privacy has been improved with Federated Learning approaches. However, some obstacles to guaranteeing traceability, openness, and participant contribution incentives prevent its widespread use. In this study, Ethereum blockchain technology is integrated into the data stream Kafka-ML framework, presenting a novel asynchronous and blockchain-based Federated Learning approach. By utilising Ethereum for transparent and auditable participant tracking, this integration overcomes some shortcomings such as auditability and model sharing reliability. Furthermore, Ethereum smart contracts allow for automatic reward distribution systems, which promote equitable incentive systems and increased involvement in the Federated Learning process. To demonstrate its potential, an extensive evaluation has been carried out on a wireless network technology detection use case. By improving transparency, traceability, and incentive structures of Federated Learning, it is expected to strengthen the robustness of flexible machine learning collaboration with data streams.
Keywords: Datos masivos; Data streams; Deep learning; Federated learning; Blockchain