Federated learning with blockchain-based model aggregation and incentives

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.

联邦学习（Federated Learning）是一种隐私保护型机器学习技术，可使相互不信任的参与方在不共享自身数据的前提下协同训练模型。绝大多数联邦学习技术都需要依托中央聚合器，存储并聚合来自多个参与方的模型参数。然而，这类中心化实体存在单点故障问题；此外，联邦学习还存在通过模型更新泄露敏感数据的风险。为解决上述两类问题，本文提出一种面向联邦学习的区块链（Blockchain）协议。该协议以区块链作为模型聚合器，解决单点故障问题；同时，我们采用基于区块链的隐私保护技术，规避数据泄露风险；此外，本协议还集成了基于区块链的激励分配模块，用于向模型贡献方发放相应激励。我们采用知名公开数据集开展对照实验，结果表明所提协议的模型精度与采用中央聚合器的联邦学习模型相当。此外，我们通过在以太坊（Ethereum）区块链上部署并运行该协议，验证了本协议的区块链运行开销情况。