GFL: A Decentralized Federated Learning Framework Based On Blockchain

TL;DR

This paper introduces GFL, a blockchain-based decentralized federated learning framework that enhances communication efficiency, security, and robustness against malicious attacks through innovative algorithms and system integration.

Contribution

GFL combines consistent hashing, RDFL, IPFS, and blockchain to improve decentralized federated learning's communication, security, and robustness.

Findings

GFL improves communication performance in federated learning.

GFL enhances robustness against malicious node attacks.

GFL performs well with Non-IID datasets.

Abstract

Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed. However, it is of great challenge for current FL frameworks to improve communication performance and maintain the security and robustness under malicious node attacks. In this paper, we propose Galaxy Federated Learning Framework(GFL), a decentralized FL framework based on blockchain. GFL introduces the consistent hashing algorithm to improve communication performance and proposes a novel ring decentralized FL algorithm(RDFL) to improve decentralized FL performance and bandwidth utilization. In addition, GFL introduces InterPlanetary File System(IPFS) and blockchain to further improve communication efficiency and FL security. Our experiments show that GFL improves communication performance and decentralized FL performance under the data poisoning of malicious nodes and non-independent and identically distributed(Non-IID) datasets.