Federated learning with blockchain-based model aggregation and incentives

Abstract

Federated learning is a privacy-preserving machine learning technique that allows mutually distrusting parties to collaboratively train a model without sharing their data. Most federated learning techniques require a centralized aggregator that stores and aggregates models received from multiple parties. However, having a centralized entity may lead to a single point of failure problem. Another problem of federated learning is the leakage of sensitive data through model updates. To address these issues, we propose a Blockchain-based protocol for federated learning. Our protocol uses Blockchain as a model aggregator solving single-point-of-failure problems. Also, we use a Blockchain-based privacy-preserving technique to avoid data leakage problems. We also incorporate a Blockchain-based incentive distribution module to distribute incentives to model contributors. We perform experiments with well-known datasets and show that the proposed model's accuracy is close to that of a centralized aggregator. We also show the overhead of Blockchain by implementing the protocol and running it on Ethereum Blockchain.