论文标题
支持区块链的联合学习的资源管理：一种深厚的加强学习方法
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
论文作者
论文摘要
支持区块链的联合学习（BFL）使移动设备能够协作训练机器学习模型所有者（MLMO）所需的神经网络模型，同时将数据保存在移动设备上。然后，模型更新以分散且可靠的方式存储在区块链中。但是，BFL的问题是移动设备具有能源和CPU的限制，可以降低系统的寿命和训练效率。另一个问题是，由于区块链采矿过程，训练潜伏期可能会增加。为了解决这些问题，MLMO需要（i）确定移动设备用于培训的数据和能量的数量，以及（ii）确定块生成率，以最大程度地减少系统潜伏期，能源消耗和激励成本，同时实现模型的目标准确性。在BFL环境的不确定性下，MLMO确定最佳决策是一项挑战。我们建议使用深度加固学习（DRL）来得出MLMO的最佳决策。
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.
