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

arXiv: https://arxiv.org/abs/2202.03575 · HTML: https://mlsanity.com/paper/2202.03575/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Revisiting Distributed Synchronous SGD](https://mlsanity.com/paper/1702.05800/) (2017)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://mlsanity.com/paper/2002.10671/) (2020)
- [Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach](https://mlsanity.com/paper/2007.09712/) (2020)
- [A Secure Federated Learning Framework for 5G Networks](https://mlsanity.com/paper/2005.05752/) (2020)
- [Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning](https://mlsanity.com/paper/2005.06105/) (2020)
- [Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1812.03633/) (2018)
- [Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks](https://mlsanity.com/paper/2011.08449/) (2020)

## Built on by (most cited first, 15 of 15)

- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing](https://mlsanity.com/paper/2407.08462/) (2024)
- [A Fair Federated Learning Framework With Reinforcement Learning](https://mlsanity.com/paper/2205.13415/) (2022)
- [Federated clustering with GAN-based data synthesis](https://mlsanity.com/paper/2210.16524/) (2022)
- [BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection](https://mlsanity.com/paper/2212.11805/) (2022)
- [FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning](https://mlsanity.com/paper/2307.13716/) (2023)
- [Dynamic Fair Federated Learning Based on Reinforcement Learning](https://mlsanity.com/paper/2311.00959/) (2023)
- [On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning](https://mlsanity.com/paper/2405.07175/) (2024)
- [FedSlate:A Federated Deep Reinforcement Learning Recommender System](https://mlsanity.com/paper/2409.14872/) (2024)
- [Personalized Federated Learning for Generative AI-Assisted Semantic Communications](https://mlsanity.com/paper/2410.02450/) (2024)
- [Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles](https://mlsanity.com/paper/2401.03159/) (2024)
- [Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain](https://mlsanity.com/paper/2407.19428/) (2024)
- [Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study](https://mlsanity.com/paper/2410.11612/) (2024)
- [PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning](https://mlsanity.com/paper/2506.15923/) (2025)

## 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.