LAFED: A lightweight authentication mechanism for blockchain-enabled federated learning system

Federated learning, as an emerging distributed machine learning technology, can use cross-device data to train a usable and secure shared model under the premise of protecting data privacy. However, the existing federated learning usually uploads the intermediate parameters to the central server to achieve model aggregation, which will cause significant privacy leakage. Recently, blockchain technology has become a research hotspot due to its advantages of decentralized and non-tampering features, providing new ideas for the realization of security certification for federated learning. However, blockchain-enabled federated learning also faces the following two challenges: (1) the identity authentication relies on the central server being fully trusted and the computation cost is heavy; (2) center-less authentication faces the challenges of efficiency and privacy leakage. To solve the above challenges, we propose a lightweight authentication mechanism for blockchain-enabled federated learning system, named LAFED. The innovations of LAFED are three-fold: (1) a lightweight authentication framework for blockchain-enabled federated learning; (2) a flexible consensus algorithm with zero-knowledge proof to verify the identity of each participant; (3) an adaptive model aggregation algorithm based on the model quality and node contribution to improve the performance. Extensive experimental results demonstrate that the proposed LAFED can achieve lightweight authentication while ensuring a high model accuracy.