Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
This addresses efficiency and scalability issues for mobile device-based federated learning systems, though it is incremental as it applies an existing DRL method to a specific bottleneck.
The paper tackles resource management in blockchain-enabled federated learning by using deep reinforcement learning to optimize data usage, energy allocation, and block generation rates, achieving improvements in system latency, energy consumption, and incentive costs while meeting target model accuracy.
Blockchain-enabled Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO) while keeping data on the mobile devices. Then, the model updates are stored in the blockchain in a decentralized and reliable manner. However, the issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency. The other issue is that the training latency may increase due to the blockchain mining process. To address these issues, the MLMO needs to (i) decide how much data and energy that the mobile devices use for the training and (ii) determine the block generation rate to minimize the system latency, energy consumption, and incentive cost while achieving the target accuracy for the model. Under the uncertainty of the BFL environment, it is challenging for the MLMO to determine the optimal decisions. We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for the MLMO.