Deep Reinforcement Learning for Resource Allocation in Blockchain-based Federated Learning

With the development of artificial intelligence, more and more applications rely on a large amount of high-quality data. Due to data island and security concerns, most of data is scattered on various devices and difficult to obtain. Federated learning (FL) is a promising paradigm to allow distributed devices cooperating to train a shared model without sharing raw data. However, the traditional FL is easy to be attacked because of single-point failure and it cannot avoid devices uploading fake or low-quality model updates. To this end, blockchain is integrated into FL to establish a secure model training ecosystem by maintaining an immutably distributed ledger. However, different data quality of raw data, diverse energy resources of devices, and different trust degree of devices make it challenging for blockchain-enabled FL efficient and reliable. Therefore, in this paper, we design a fine-grained resource allocation scheme for blockchain-enabled FL with considering the credit of devices, data quality, and energy resources. We first propose a credit-based blockchain-enabled FL to jointly execute FL training and blockchain establishment. Then we formulate the resource allocation problem with considering credit, data quality, precision, latency, and energy resources. A deep-reinforcement learning based algorithm is designed to solve the problem, and BlockSim is used to build the blockchain-enabled FL platform. Simulation results demonstrate the effectiveness of our proposed scheme on precision, latency and energy consumption, compared with traditional blockchain-enabled FL.