  Federated learning has been widely studied and applied to various scenarios.
In mobile computing scenarios, federated learning protects users from exposing
their private data, while cooperatively training the global model for a variety
of real-world applications. However, the security of federated learning is
increasingly being questioned, due to the malicious clients or central servers'
constant attack to the global model or user privacy data. To address these
security issues, we proposed a decentralized federated learning framework based
on blockchain, i.e., a Blockchain-based Federated Learning framework with
Committee consensus (BFLC). The framework uses blockchain for the global model
storage and the local model update exchange. To enable the proposed BFLC, we
also devised an innovative committee consensus mechanism, which can effectively
reduce the amount of consensus computing and reduce malicious attacks. We then
discussed the scalability of BFLC, including theoretical security, storage
optimization, and incentives. Finally, we performed experiments using
real-world datasets to verify the effectiveness of the BFLC framework.
