mlsanityData to 7 Oct 2026

Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems

arXiv 2101.03367 ↗First posted 9 Jan 2021cs.LG cs.DCcited by 45

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Abstract (arXiv)

Next-generation autonomous and networked industrial systems (i.e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computing. These networked multi-agent systems require fast, communication-efficient and distributed machine learning (ML) to provide mission critical control functionalities. Distributed ML techniques, including federated learning (FL), represent a mushrooming multidisciplinary research area weaving in sensing, communication and learning. FL enables continual model training in distributed wireless systems: rather than fusing raw data samples at a centralized server, FL leverages a cooperative fusion approach where networked agents, connected via URLLC, act as distributed learners that periodically exchange their locally trained model parameters. This article explores emerging opportunities of FL for the next-generation networked industrial systems. Open problems are discussed, focusing on cooperative driving in connected automated vehicles and collaborative robotics in smart manufacturing.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 9 Jan 2021.

Builds on

11 of 20 references.

References · 11

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  3. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  4. 2019Federated Learning over Wireless Fading Channelscited by 178
  5. 2018Blockchained On-Device Federated Learningcited by 105
  6. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
  7. 2020Decentralized Federated Learning via SGD over Wireless D2D Networkscited by 32
  8. 2019GADMM: Fast and Communication Efficient Framework for Distributed Machine Learningcited by 30
  9. 2020Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learningcited by 26
  10. 2018Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scalecited by 22
  11. 2018GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descentcited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 9; 2023: 14; 2024: 7; 2025: 5; 2026: 2.

45 papers.

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  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  2. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  3. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  4. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  5. 2021Over-the-Air Decentralized Federated Learningcited by 18
  6. 2023A Survey of Federated Learning for Connected and Automated Vehiclescited by 17
  7. 2022An Energy and Carbon Footprint Analysis of Distributed and Federated Learningcited by 16
  8. 2023On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharingcited by 16
  9. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
  10. 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
  11. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
  12. 2021On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSIcited by 9
33 more
  1. 2021A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learningcited by 8
  2. 2023Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networkscited by 8
  3. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  4. 2021Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancycited by 7
  5. 2023Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Drivingcited by 7
  6. 2021On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestratorcited by 6
  7. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  8. 2022SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networkscited by 6
  9. 2023Gossiped and Quantized Online Multi-Kernel Learningcited by 6
  10. 2022Towards a Defense Against Federated Backdoor Attacks Under Continuous Trainingcited by 5
  11. 2024Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systemscited by 5
  12. 2025Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integrationcited by 5
  13. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  14. 2023A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsificationcited by 3
  15. 2023Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionalitycited by 3
  16. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  17. 2023Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networkscited by 2
  18. 2024Initialisation and Network Effects in Decentralised Federated Learningcited by 2
  19. 2024Combining Federated Learning and Control: A Surveycited by 2
  20. 2025Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resiliencecited by 2
  21. 2021Integrating Sensing and Communication in Cellular Networks via NR Sidelinkcited by 1
  22. 2022Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Datacited by 1
  23. 2022On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learningcited by 1
  24. 2025A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networkscited by 1
  25. 2023Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehiclescited by 0
  26. 2023Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learningcited by 0
  27. 2024Poisoning Attacks on Federated Learning for Autonomous Drivingcited by 0
  28. 2024Cooperation and Federation in Distributed Radar Point Cloud Processingcited by 0
  29. 2024Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoTcited by 0
  30. 2025Principles and Components of Federated Learning Architecturescited by 0
  31. 2025Bayesian Federated Learning for Continual Trainingcited by 0
  32. 2026Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuningcited by 0
  33. 2026An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networkscited by 0