StateFL: State Channels Powered Federated Learning

Abstract

Federated Learning (FL) is a decentralized machine learning approach that provides a privacy-friendly way of training models by keeping the datasets of participating parties private. Some challenges FL faces are the lack of incentives to encourage participation in the learning process, as well as preventing potential cyber attacks that tamper with the model. Blockchain is an available solution that provides the means to implement incentives to encourage participation and issue penalties to disincentivize malicious behavior. Hence, recent developments introduced blockchain-enabled FL (BCFL) designs for various applications. However, one obstacle that slows down the widespread adoption of this technology is the high latency of blockchain networks due to its laborious consensus protocols. In this paper, we propose StateFL, a revised BCFL architecture that uses state channels (a blockchain scaling solution) in order to ease the load on the blockchain by reducing the number of on-chain transactions, improving the system’s latency, and minimizing transaction fees as a result. State channels are governed by smart contracts and enable two parties to exchange information and assets off-chain unless disputes occur. Either channel party can dispute the state of the channel if suspicious behavior is observed. In that case, the dispute is settled on the blockchain. We evaluate StateFL in a series of experiments to establish latency improvements, identify bottlenecks, and quantify the impact of disputes on channel latency and transaction fees. The findings show that the higher the number of FL rounds, the more StateFL outperforms the baseline BCFL, with the exception of a very low number of rounds. In realistic FL scenarios, the rounds are in the order of hundreds making StateFL a solid contender even if disputes do occasionally occur. The bottleneck of StateFL is the channel setup and closure which require extensive interaction with the blockchain.