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

Abstract

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 research focusing on the allocation of resources for clients in BCFL. In the BCFL framework where the FL clients and the blockchain miners are the same devices, clients 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 into training and mining needs to be carefully addressed. In this paper, we design an incentive mechanism to 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 model owner (MO) (i.e., the BCFL task publisher) 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.

Resource Optimization for Blockchain-based Federated Learning in Mobile Edge Computing

“…Since the resources of edge servers are usually limited, it is essential to design a resource allocation scheme for edge servers to provide satisfactory services for both the MEC and the BCFL tasks with the minimum cost. Wang et al [28] design a joint resource allocation mechanism in BCFL, which assists the participants in deciding the proper resources for completing training and mining tasks. In [29], a hybrid blockchain-assisted resource trading system is designed to achieve the decentralization and efficiency for FL in MEC.…”