A Blockchain-Based Decentralized Federated Learning Framework with Committee Consensus

TLDR

Federated learning protects user privacy by exchanging only model gradients or updates, yet its security is threatened by malicious clients or central servers. This work introduces a decentralized federated learning framework that leverages blockchain and a committee consensus mechanism to mitigate such security risks. The framework stores the global model on a blockchain, exchanges local updates through it, and employs a lightweight committee consensus to reduce computation and defend against attacks, while addressing scalability, security, storage optimization, and incentives, and is evaluated on a FISCO blockchain with AlexNet on the FEMNIST dataset. Experiments show that the BFLC framework effectively preserves security and improves robustness.

Abstract

Federated learning has been widely studied and applied to various scenarios, such as financial credit, medical identification, and so on. Under these settings, federated learning protects users from exposing their private data, while cooperatively training a shared machine learning algorithm model (i.e., the global model) for a variety of realworld applications. The only data exchanged is the gradient of the model or the updated model (i.e., the local model update). However, the security of federated learning is increasingly being questioned, due to the malicious clients or central servers' constant attack on the global model or user privacy data. To address these security issues, we propose a decentralized federated learning framework based on blockchain, that is, a Block-chain-based Federated Learning framework with Committee consensus (BFLC). Without a centralized server, the framework uses blockchain for the global model storage and the local model update exchange. To enable the proposed BFLC, we also devise an innovative committee consensus mechanism, which can effectively reduce the amount of consensus computing and reduce malicious attacks. We then discuss the scalability of BFLC, including theoretical security, storage optimization, and incentives. Finally, based on a FISCO blockchain system, we perform experiments using an AlexNet model on several frameworks with a real-world dataset FEMNIST. The experimental results demonstrate the effectiveness and security of the BFLC framework.