Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning

• Published in: IEEE transactions on parallel and distributed systems Vol. 34; no. 5; pp. 1 - 12
• Main Authors: Wang, Zhilin, Hu, Qin, Li, Ruinian, Xu, Minghui, Xiong, Zehui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blockchain; Blockchains; Clients; Computation; Computational modeling; Cryptography; Data models; Exact solutions; Federated learning; Game theory; Games; incentive mechanism; Optimization; Resource allocation; Resource management; Task analysis; Training
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2023.3253604

Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages, such as decentralization and privacy protection of raw data. However, there has been few studies focusing on the allocation of resources for the participated devices (i.e., clients) in the BCFL system. Especially, in the BCFL framework where the FL clients are also the blockchain miners, clients have to train the local models, broadcast the trained model updates to the blockchain network, and then perform mining to generate new blocks. Since each client has a limited amount of computing resources, the problem of allocating computing resources to training and mining needs to be carefully addressed. In this paper, we design an incentive mechanism to help the model owner (MO) (i.e., the BCFL task publisher) assign each client appropriate rewards for training and mining, and then the client will determine the amount of computing power to allocate for each subtask based on these rewards using the two-stage Stackelberg game. After analyzing the utilities of the MO and clients, we transform the game model into two optimization problems, which are sequentially solved to derive the optimal strategies for both the MO and clients. Further, considering the fact that local training related information of each client may not be known by others, we extend the game model with analytical solutions to the incomplete information scenario. Extensive experimental results demonstrate the validity of our proposed schemes.