A Blockchain-Empowered Incentive Mechanism for Cross-Silo Federated Learning

【Author】 Tang, Ming; Peng, Fu; Wong, Vincent W. S.

【Source】IEEE TRANSACTIONS ON MOBILE COMPUTING

【影响因子】6.075

【Abstract】In cross-silo federated learning (FL), organizations cooperatively train a global model with their local datasets. However, some organizations may act as free riders such that they only contribute a small amount of resources but can obtain a high-accuracy global model. Meanwhile, some organizations can be business competitors, and they do not trust each other or any third-party entity. In this work, our goal is to design a framework that motivates efficient cooperation among organizations without the coordination of a central entity. To this end, we propose a blockchain-empowered incentive mechanism framework for cross-silo FL. Under this incentive mechanism framework, we develop a distributed algorithm that enables organizations to achieve social efficiency, individual rationality, and budget balance without private information of the organizations. Our proposed algorithm has a proven convergence guarantee and empirically achieves a higher convergence rate than a benchmark method. Moreover, we propose a transaction minimization algorithm to reduce the number of transactions made among organizations in the blockchain. This algorithm is proven to achieve a performance no worse than twice the minimum value. The experimental results in a testbed show that our proposed framework enables organizations to achieve social efficiency within a relatively short iterative process.

【Keywords】Organizations; Training; Costs; Load modeling; Smart contracts; Servers; Blockchain; federated learning; game theory; incentive mechanism; resource allocation; resource allocation

【发表时间】2024 OCT

【收录时间】2024-09-18

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-联邦学习