Secure solution for decentralized federated learning with blockchain
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.
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