High-Quality Model Aggregation for Blockchain-Based Federated Learning via Reputation-Motivated Task Participation

【Author】 Qi, Jiahao; Lin, Feilong; Chen, Zhongyu; Tang, Changbing; Jia, Riheng; Li, Minglu

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Federated learning is an emerging paradigm to conduct the machine learning collaboratively but avoid the leakage of original data. Then, how to motivate the data owners to participate federated learning and contribute high-quality data is the crucial issue. In this article, a blockchain-based federated learning (BFL) with a reputation mechanism for high-quality model aggregation is proposed. Specifically, the blockchain transforms the federated learning into a decentralized and trustworthy manner. Over the blockchain, federated learning tasks, undertaken by smart contracts, can be conducted transparently and fairly. Besides, a reputation-constrained data contribution and reward allocation mechanism is designed to encourage data owners to participate in BFL and contribute high-quality data. The noncooperative game is adopted to analyze the behavior strategies of data owners. The existence of the unique equilibrium is proved and the equilibrium point indicates that the data owners can acquire highest reward with the contribution of the highest quality data. Thus, the model quality of BFL is guaranteed. Finally, simulations on the public data sets (MNIST and CIFAR10) demonstrate that BFL with a reputation mechanism can well promote the high-quality model aggregation of federated learning as well as can prevent malicious nodes from corrupting the training task.

【Keywords】Collaborative work; Data models; Task analysis; Blockchains; Training; Computational modeling; Smart contracts; Blockchain; federated learning; high-quality model; reputation mechanism; smart contract

【发表时间】2022 OCT 1

【收录时间】2022-10-05

【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