论文标题
GFL：基于区块链的分散联合学习框架
GFL: A Decentralized Federated Learning Framework Based On Blockchain
论文作者
论文摘要
联邦学习（FL）是一个快速增长的领域，已经提出了许多集中和分散的FL框架。但是，对于当前的FL框架，提高沟通性能并保持恶意节点攻击下的安全性和鲁棒性是巨大的挑战。在本文中，我们提出了联合学习框架（GFL）的Galaxy，这是一个基于区块链的分散的FL框架。 GFL引入了一致的哈希算法，以提高通信性能，并提出了一种新型的环形FL算法（RDFL），以改善分散的FL性能和带宽利用率。此外，GFL引入了行星际文件系统（IPFS）和区块链，以进一步提高沟通效率和FL安全性。我们的实验表明，在恶意节点的数据中毒以及非独立和相同分布的（非IID）数据集中，GFL改善了通信性能和分散的FL性能。
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.
