mlsanityData to 14 Oct 2026

Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks

arXiv 2603.16881 ↗First posted 24 Feb 2026cs.LGcited by 0

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Abstract (arXiv)

Multi-agent deep learning (MADL), including multi-agent deep reinforcement learning (MADRL), distributed/federated training, and graph-structured neural networks, is becoming a unifying framework for decision-making and inference in wireless systems where sensing, communication, and computing are tightly coupled. Recent 5G-Advanced and 6G visions strengthen this coupling through integrated sensing and communication, edge intelligence, open programmable RAN, and non-terrestrial/UAV networking, which create decentralized, partially observed, time-varying, and resource-constrained control problems. This survey synthesizes the state of the art, with emphasis on 2021-2025 research, on MADL for distributed sensing and wireless communications. We present a task-driven taxonomy across (i) learning formulations (Markov games, Dec-POMDPs, CTDE), (ii) neural architectures (GNN-based radio resource management, attention-based policies, hierarchical learning, and over-the-air aggregation), (iii) advanced techniques (federated reinforcement learning, communication-efficient federated deep RL, and serverless edge learning orchestration), and (iv) application domains (MEC offloading with slicing, UAV-enabled heterogeneous networks with power-domain NOMA, intrusion detection in sensor networks, and ISAC-driven perceptive mobile networks). We also provide comparative tables of algorithms, training topologies, and system-level trade-offs in latency, spectral efficiency, energy, privacy, and robustness. Finally, we identify open issues including scalability, non-stationarity, security against poisoning and backdoors, communication overhead, and real-time safety, and outline research directions toward 6G-native sense-communicate-compute-learn systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 24 Feb 2026.

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20 of 35 references.

References · 20

  1. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  2. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  3. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  4. 2021Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyondcited by 91
  5. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  6. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  7. 2020Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorialcited by 60
  8. 2021Hierarchical Federated Learning with Quantization: Convergence Analysis and System Designcited by 47
  9. 2022Graph Neural Networks for Wireless Communications: From Theory to Practicecited by 46
  10. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 42
  11. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  12. 2020Federated Learning and Wireless Communicationscited by 27
8 more
  1. 2022A Survey on Over-the-Air Computationcited by 21
  2. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  3. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 18
  4. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  5. 2022Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directionscited by 8
  6. 2022Collaborative Sensing in Perceptive Mobile Networks: Opportunities and Challengescited by 3
  7. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  8. 2023Cross-Edge Orchestration of Serverless Functions with Probabilistic Cachingcited by 1

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  1. 2024Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning
  2. 2025Multi-Agent Deep Reinforcement Learning for UAV-Assisted 5G Network Slicing: A Comparative Study of MAPPO, MADDPG, and MADQN ↗
  3. 2025Reducing Variance Caused by Communication in Decentralized Multi-agent Deep Reinforcement Learning
  4. 2026Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directions
  5. 2025Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G
  6. 2025Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks
  7. 2025Distributed Coordination for Heterogeneous Non-Terrestrial Networks
  8. 2026Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications