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.

翻译：联邦学习(FL)是一个迅速增长的领域,已经提出了许多中央和分散的FL框架,但是,对于目前的FL框架来说,在恶意节点攻击下,改善通信绩效和保持安全和稳健是巨大的挑战,在本文件中,我们提出银河联邦学习框架(FFL),这是一个以街区链为基础的分散的FL框架。GFL采用一贯的散射算法来提高通信绩效,并提出一种新的环形分散的FL算法(RDFL),以改善分散的FL的功能和带宽利用。此外,GLL引入了InterPlanetary File系统(IPFS)和块链来进一步提高通信效率和FL的安全性。我们的实验表明,GLF在恶意节点、不独立和同样分布的数据下,GFL改进了通信绩效和分散的FL业绩。