GFL: A Decentralized Federated Learning Framework Based On Blockchain
- URL: http://arxiv.org/abs/2010.10996v3
- Date: Tue, 13 Apr 2021 14:05:31 GMT
- Title: GFL: A Decentralized Federated Learning Framework Based On Blockchain
- Authors: Yifan Hu, Yuhang Zhou, Jun Xiao, Chao Wu
- Abstract summary: 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.
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.
- Score: 15.929643607462353
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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