mlsanityData to 7 Oct 2026

Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT

arXiv 2202.03575 ↗First posted 3 Feb 2022cs.LGcited by 15

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Abstract (arXiv)

The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning (ML) paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST and CIFAR-10 data sets to represent the data generated by IIoT. During the experiment, we employ the deep neural network (DNN) model to train the data, and experimental results show that the accuracy can reach more than 97%, which corroborates the effectiveness of the proposed algorithm.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 12 of its 39 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

12 of 39 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  3. 2017Revisiting Distributed Synchronous SGDcited by 142
  4. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  5. 2019Reliable Federated Learning for Mobile Networkscited by 92
  6. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  7. 2020Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Frameworkcited by 59
  8. 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
  9. 2020A Secure Federated Learning Framework for 5G Networkscited by 32
  10. 2020Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learningcited by 10
  11. 2018Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approachcited by 9
  12. 2020Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networkscited by 5

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Citing papers per year: 2022: 3; 2023: 3; 2024: 7; 2025: 2.

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  1. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  2. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  3. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
  4. 2022A Fair Federated Learning Framework With Reinforcement Learningcited by 6
  5. 2022Federated clustering with GAN-based data synthesiscited by 3
  6. 2022BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selectioncited by 1
  7. 2023FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learningcited by 1
  8. 2023Dynamic Fair Federated Learning Based on Reinforcement Learningcited by 1
  9. 2024On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learningcited by 1
  10. 2024FedSlate:A Federated Deep Reinforcement Learning Recommender Systemcited by 1
  11. 2024Personalized Federated Learning for Generative AI-Assisted Semantic Communicationscited by 1
  12. 2024Distributed client selection with multi-objective in federated learning assisted Internet of Vehiclescited by 0
3 more
  1. 2024Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchaincited by 0
  2. 2024Federated Learning framework for LoRaWAN-enabled IIoT communication: A case studycited by 0
  3. 2025PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learningcited by 0