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.
@article{arxiv.2004.04104,
title = {Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach},
author = {Nguyen Quang Hieu and Tran The Anh and Nguyen Cong Luong and Dusit Niyato and Dong In Kim and Erik Elmroth},
journal= {arXiv preprint arXiv:2004.04104},
year = {2020}
}