Paper Info
Title
ChainsFL: Blockchain-driven Federated Learning from Design to Realization
Abstract
Despite the advantages of Federated Learning (FL), such as devolving model training to intelligent devices and preserving data privacy, FL still faces the risk of the single point of failure and attack from malicious participants. Recently, blockchain is considered a promising solution that can transform FL training into a decentralized manner and improve security during training. However, traditional consensus mechanisms and architecture for blockchain can hardly handle the large-scale FL task due to the huge resource consumption, limited throughput, and high communication complexity. To this end, this paper proposes a two-layer blockchain-driven FL framework, called as ChainsFL, which is composed of multiple Raft-based shard networks (layer-1) and a Direct Acyclic Graph (DAG)-based main chain (layer-2) where layer-1 limits the scale of each shard for a small range of information exchange, and layer-2 allows each shard to update and share the model in parallel and asynchronously. Furthermore, FL procedure in a blockchain manner is designed, and the refined DAG consensus mechanism to mitigate the effect of stale models is proposed. In order to provide a proof-of-concept implementation and evaluation, the shard blockchain base on Hyperledger Fabric is deployed on the self-made gateway as layer-1, and the self-developed DAG-based main chain is deployed on the personal computer as layer-2. The experimental results show that ChainsFL provides acceptable and sometimes better training efficiency and stronger robustness comparing with the typical existing FL systems.
Year
DOI
Venue
2021
10.1109/WCNC49053.2021.9417299
2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
DocType
ISSN
Citations 
Conference
1525-3511
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Shuo Yuan101.01
Bin Cao2675.06
Mugen Peng32779200.37
Yaohua Sun41539.72