mlsanityData to 7 Oct 2026

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

arXiv 2202.01512 ↗First posted 3 Feb 2022cs.LG cs.AI cs.DCcited by 25

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Abstract (arXiv)

Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate on decentralized devices in modern factories. To protect the confidentiality of industrial data, federated learning (FL) was introduced to collaboratively train shared machine learning models. However, the local data collected by different devices skew in class distribution and degrade industrial FL performance. This challenge has been widely studied at the mobile edge, but they ignored the rapidly changing streaming data and clustering nature of factory devices, and more seriously, they may threaten data security. In this paper, we propose FedGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i.i.d. data. Taking advantage of naturally clustered factory devices, FedGS uses a gradient-based binary permutation algorithm (GBP-CS) to select a subset of devices within each factory and build homogeneous super nodes participating in FL training. Then, we propose a compound-step synchronization protocol to coordinate the training process within and among these super nodes, which shows great robustness against data heterogeneity. The proposed methods are time-efficient and can adapt to dynamic environments, without exposing confidential industrial data in risky manipulation. We prove that FedGS has better convergence performance than FedAvg and give a relaxed condition under which FedGS is more communication-efficient. Extensive experiments show that FedGS improves accuracy by 3.5% and reduces training rounds by 59% on average, confirming its superior effectiveness and efficiency on non-i.i.d. data.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 28 of its 53 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

28 of 53 references.

References · 28

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
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  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
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  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  6. 2018Federated Learning with Non-IID Datacited by 1,099
  7. 2020Adaptive Federated Optimizationcited by 1,036
  8. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
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16 more
  1. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  2. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  3. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
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  5. 2018AdaGrad stepsizes: Sharp convergence over nonconvex landscapescited by 182
  6. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  7. 2020Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approachcited by 56
  8. 2020Inverse Distance Aggregation for Federated Learning with Non-IID Datacited by 44
  9. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  10. 2019Real-World Image Datasets for Federated Learningcited by 30
  11. 2020When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Networkcited by 29
  12. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  13. 2019Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updatingcited by 22
  14. 2022Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Trainingcited by 7
  15. 2020Industrial Federated Learning -- Requirements and System Designcited by 6
  16. 2020Client Adaptation improves Federated Learning with Simulated Non-IID Clientscited by 3

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Citing papers per year: 2022: 4; 2023: 12; 2024: 3; 2025: 6.

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  1. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  2. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  3. 2023DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learningcited by 23
  4. 2022HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaversecited by 14
  5. 2023MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulationcited by 13
  6. 2022Distributed Pruning Towards Tiny Neural Networks in Federated Learningcited by 11
  7. 2023Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networkscited by 8
  8. 2022SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligencecited by 7
  9. 2023Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoTcited by 6
  10. 2025Rethinking Federated Graph Learning: A Data Condensation Perspectivecited by 6
  11. 2023StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participationcited by 4
  12. 2023Multi-Tier Client Selection for Mobile Federated Learning Networkscited by 3
13 more
  1. 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
  2. 2023Analysis and Optimization of Wireless Federated Learning with Data Heterogeneitycited by 3
  3. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  4. 2024Federated Learning Clients Clustering with Adaptation to Data Driftscited by 3
  5. 2023Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networkscited by 2
  6. 2023Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Servicecited by 2
  7. 2025Towards One-shot Federated Learning: Advances, Challenges, and Future Directionscited by 2
  8. 2024Federated Learning with Only Positive Labels by Exploring Label Correlationscited by 1
  9. 2024Federated Cross-Training Learners for Robust Generalization under Data Heterogeneitycited by 1
  10. 2025Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learningcited by 1
  11. 2025Communication Efficient Adaptive Model-Driven Quantum Federated Learningcited by 1
  12. 2025Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Trainingcited by 0
  13. 2025DAG-AFL:Directed Acyclic Graph-based Asynchronous Federated Learningcited by 0