Deep Reinforcement Learning for Resource Management in Blockchain-Enabled Federated Learning Network
Nguyen Quang Hieu, The Anh Tran, Cong Luong Nguyen, Dusit Niyato, Dong In Kim, Erik Elmroth
Abstract
Blockchain-enabled Federated Learning (BFL) enables model updates to be stored in blockchain in a reliable manner. However, one problem is the increase of the training latency due to the mining process. Moreover, mobile devices have energy and CPU constraints. Therefore, the machine learning model owner (MLMO) needs to decide the data and energy that the mobile devices use for the training and determine the block generation rate to minimize the system latency and mining cost while achieving the target accuracy. Under the uncertainty of BFL, we propose to use deep reinforcement learning to find the optimal decisions for the MLMO.
Topics & Concepts
BlockchainReinforcement learningComputer scienceLatency (audio)Deep learningBlock (permutation group theory)Artificial intelligenceProcess (computing)Distributed computingMobile deviceEmbedded systemMachine learningReal-time computingComputer securityOperating systemTelecommunicationsGeometryMathematicsPrivacy-Preserving Technologies in DataBlockchain Technology Applications and SecurityAge of Information Optimization