RT Conference Proceedings
T1 Federated Learning Meets Blockchain: A Kafka-ML Integration for reliable model training using data streams
A1 Martín-Fernández, Cristian
A1 Chaves, Antonio
A1 Díaz-Rodríguez, Manuel
A1 Shahid, Adnan
A1 SKIm, Kwang Soon
K1 Datos masivos
AB 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 integrationovercomes 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.
PB IEEE
YR 2024
FD 2024
LK https://hdl.handle.net/10630/36952
UL https://hdl.handle.net/10630/36952
LA eng
NO Chaves, A. J., Martín, C., Kim, K. S., Shahid, A., & Díaz, M. (2024, December). Federated Learning Meets Blockchain: A Kafka-ML Integration for reliable model training using data streams. In 2024 IEEE International Conference on Big Data (BigData) (pp. 7677-7686). IEEE.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026