  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.
