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

A team of researchers including Nadine Muller and Su Zhang has published a seminal survey on arXiv, cataloging the rapid convergence of Federated Learning and Multi-Agent Deep Learning (MADL) as a foundational framework for next-generation wireless networks. The paper, 'Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks,' acts as a unifying guide for a field being driven by 5G-Advanced and 6G visions, which tightly integrate sensing, communication, and computing. The authors systematically review research from 2021-2025, addressing the complex, decentralized control problems created by technologies like Integrated Sensing and Communication (ISAC), edge intelligence, and open RAN.

The survey's core contribution is a detailed, task-driven taxonomy that organizes the field across four critical dimensions. First, it covers learning formulations like Markov games and Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs). Second, it details neural architectures, including Graph Neural Networks (GNNs) for radio resource management and attention-based policies. Third, it examines advanced techniques such as communication-efficient federated deep reinforcement learning. Finally, it maps these technologies to concrete application domains like UAV-enabled networks, Mobile Edge Computing (MEC) offloading, and perceptive mobile networks driven by ISAC.

Beyond categorization, the paper provides valuable comparative analysis of algorithms and system-level trade-offs in latency, spectral efficiency, and energy use. It concludes by identifying pressing open challenges that must be solved for real-world adoption. These include scalability in massive networks, handling non-stationary environments, defending against security threats like data poisoning, reducing communication overhead, and ensuring real-time safety. The survey ultimately charts a research path toward building 6G-native systems that can seamlessly sense, communicate, compute, and learn in a distributed manner.