Secure solution for decentralized federated learning with blockchain

Abstract

As a promising paradigm of distributed learning, federated learning has garnered considerable attention since its emergence. However, traditional federated learning solutions based on a central server are not efficient and scalable. Moreover, the centralized design relies on a trustworthy party coordinating participants. This also leads to trust and reliability issues, such as a compromised central server or a single-point failure. To address this issue, blockchain-based federated learning has been proposed as a decentralized variant. Blockchain-based decentralized federated learning seems promising. However, a new attack surface appears. Because blockchain records each transaction on a public ledger, all peers can obtain a legal copy of the local model of each participant, severely violating the privacy and interests of the participants. Challenged by this dilemma, we provide an alternative design for secure federated learning in a decentralized way, addressing data confidentiality and fairness issues simultaneously. Unlike previous studies, we construct a produce-and-consume model for parameter aggregation on a blockchain, auditing the behavior of participants in case of free-riding and false-reporting attacks. Furthermore, we design a consensus protocol called APoS, which provides an incentive and review mechanism and enforces honest training of federated learning participants.