OpenFL: A scalable and secure decentralized federated learning system on the Ethereum blockchain

Decentralized Federated Learning (FL) offers a paradigm where independent entities collaboratively train a machine learning model while preserving the privacy of their datasets. Integrating blockchain technology into decentralized FL frameworks is critical to establishing the trust necessary for user participation. However, existing FL systems using blockchain often struggle with scalability, latency, and privacy issues, particularly in permissionless blockchain contexts. This paper proposes OpenFL, a novel, collateral-backed reputation system implemented on the Ethereum blockchain. This system aims to foster trust among participants in a decentralized FL environment. We present a fully autonomous smart contract platform specifically tailored to facilitate FL processes among anonymous users. Furthermore, we address potential security concerns by detailing our strategies to mitigate various attack vectors. To validate our system's efficacy, we conducted experiments on the Ethereum Ropsten testnet using the MNIST and CIFAR-10 datasets. Our findings demonstrate OpenFL's capability to overcome the inherent limitations of permissionless blockchains while highlighting the significance of open-access protocols in this context. OpenFL can potentially broaden the participant base in trust-sensitive applications by reducing entry barriers, thus substantially contributing to decentralized machine learning.