# Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems arXiv: https://arxiv.org/abs/2101.03367 ยท HTML: https://mlsanity.com/paper/2101.03367/ ## Key facts - Cited by 45 papers in the map (data through 14 Oct 2026). - Builds on 11 of its 20 references that are in the map. - First posted on arXiv 9 Jan 2021. ## Builds on (most cited first, 11 of 11) - [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019) - [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016) - [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018) - [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019) - [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018) - [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019) - [Decentralized Federated Learning via SGD over Wireless D2D Networks](https://mlsanity.com/paper/2002.12507/) (2020) - [GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning](https://mlsanity.com/paper/1909.00047/) (2019) - [Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning](https://mlsanity.com/paper/2001.02112/) (2020) - [Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale](https://mlsanity.com/paper/1801.01270/) (2018) - [GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent](https://mlsanity.com/paper/1803.05880/) (2018) ## Built on by (most cited first, 30 of 45) - [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022) - [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021) - [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023) - [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023) - [Over-the-Air Decentralized Federated Learning](https://mlsanity.com/paper/2106.08011/) (2021) - [A Survey of Federated Learning for Connected and Automated Vehicles](https://mlsanity.com/paper/2303.10677/) (2023) - [An Energy and Carbon Footprint Analysis of Distributed and Federated Learning](https://mlsanity.com/paper/2206.10380/) (2022) - [On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing](https://mlsanity.com/paper/2303.10695/) (2023) - [Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification](https://mlsanity.com/paper/2206.01748/) (2022) - [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022) - [Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2209.06623/) (2022) - [On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI](https://mlsanity.com/paper/2104.00331/) (2021) - [A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning](https://mlsanity.com/paper/2103.10346/) (2021) - [Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks](https://mlsanity.com/paper/2303.08361/) (2023) - [Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks](https://mlsanity.com/paper/2304.08996/) (2023) - [Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy](https://mlsanity.com/paper/2108.11769/) (2021) - [Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2306.16169/) (2023) - [On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator](https://mlsanity.com/paper/2108.02712/) (2021) - [Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions](https://mlsanity.com/paper/2110.04160/) (2021) - [SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks](https://mlsanity.com/paper/2203.14094/) (2022) - [Gossiped and Quantized Online Multi-Kernel Learning](https://mlsanity.com/paper/2301.09848/) (2023) - [Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration](https://mlsanity.com/paper/2505.11191/) (2025) - [Towards a Defense Against Federated Backdoor Attacks Under Continuous Training](https://mlsanity.com/paper/2205.11736/) (2022) - [Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems](https://mlsanity.com/paper/2405.18580/) (2024) - [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023) - [A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification](https://mlsanity.com/paper/2310.08087/) (2023) - [Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality](https://mlsanity.com/paper/2310.19287/) (2023) - [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024) - [Review of Mathematical Optimization in Federated Learning](https://mlsanity.com/paper/2412.01630/) (2024) - [Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks](https://mlsanity.com/paper/2311.04350/) (2023) ## 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.