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

TL;DR

This paper proposes an incentive mechanism for resource allocation in blockchain-based federated learning, optimizing client rewards and resource distribution through game theory to enhance system efficiency and privacy.

Contribution

It introduces a novel incentive mechanism using a two-stage Stackelberg game for resource allocation in BCFL, including solutions for incomplete information scenarios.

Findings

The proposed scheme effectively balances training and mining resources.

Experimental results validate the incentive mechanism's effectiveness.

The model improves resource utilization and privacy protection.

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.