Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks
arXiv:2603.16881v1 Announce Type: new Abstract: 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
arXiv:2603.16881v1 Announce Type: new Abstract: 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.
Executive Summary
This article provides a comprehensive survey of federated multi-agent deep learning (MADL) techniques for advanced distributed sensing in wireless networks. It synthesizes the state of the art in MADL for distributed sensing and wireless communications, focusing on research from 2021-2025. The authors present a task-driven taxonomy across various aspects of MADL, including learning formulations, neural architectures, advanced techniques, and application domains. They also provide comparative tables of algorithms, training topologies, and system-level trade-offs. The article identifies open issues and outlines research directions toward 6G-native sense-communicate-compute-learn systems. The survey is a valuable resource for researchers and practitioners seeking to understand the current landscape and future prospects of MADL in wireless networks.
Key Points
- ▸ Federated multi-agent deep learning (MADL) is a unifying framework for decision-making and inference in wireless systems.
- ▸ Recent 5G-Advanced and 6G visions strengthen the coupling of sensing, communication, and computing in wireless networks.
- ▸ The article presents a task-driven taxonomy of MADL techniques for distributed sensing and wireless communications.
Merits
Comprehensive Survey
The article provides a thorough review of the current state of the art in MADL for distributed sensing and wireless communications.
Task-Driven Taxonomy
The authors present a structured framework for understanding the various aspects of MADL, facilitating easier navigation and comparison of different techniques.
Comparative Analysis
The article includes comparative tables of algorithms, training topologies, and system-level trade-offs, enabling readers to evaluate the strengths and weaknesses of different approaches.
Demerits
Limited Focus on Theory
The article primarily focuses on applications and techniques, with relatively little discussion of theoretical foundations and underlying principles.
Limited Coverage of Security Considerations
While the article touches on security issues, it does not provide a comprehensive treatment of the topic, which is critical for the development of trustworthiness in wireless networks.
Expert Commentary
This article is a significant contribution to the field of wireless networks, providing a comprehensive survey of federated multi-agent deep learning techniques for advanced distributed sensing. The authors' task-driven taxonomy is a valuable framework for understanding the various aspects of MADL, and their comparative analysis of algorithms and system-level trade-offs is a useful resource for researchers and practitioners. However, the article could benefit from a more in-depth discussion of theoretical foundations and security considerations. Overall, this article is a must-read for anyone interested in the development of 6G-native sense-communicate-compute-learn systems.
Recommendations
- ✓ Future research should focus on developing more robust and secure federated multi-agent deep learning techniques.
- ✓ The development of theoretical foundations and underlying principles of MADL should be a priority for researchers in the field.
