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

2026-03-19 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices, Cloud Computing & IT Infrastructure · Depth: Advanced, quick

Summary

A new survey, submitted on February 24, 2026, synthesizes the state of the art in Multi-Agent Deep Learning (MADL) for distributed sensing and wireless communications, focusing on research from 2021-2025. MADL, encompassing multi-agent deep reinforcement learning (MADRL), federated training, and graph neural networks, is presented as a unifying framework for decision-making in wireless systems where sensing, communication, and computing are tightly integrated. The survey details a task-driven taxonomy covering learning formulations like Markov games and Dec-POMDPs, neural architectures such as GNN-based radio resource management and attention-based policies, and advanced techniques including federated reinforcement learning and serverless edge learning orchestration. It also explores application domains like MEC offloading, UAV-enabled networks, intrusion detection, and ISAC-driven perceptive mobile networks, providing comparative tables on system-level trade-offs. The authors identify open issues such as scalability, non-stationarity, security vulnerabilities like poisoning and backdoors, communication overhead, and real-time safety, outlining future research directions for 6G-native systems.

Key takeaway

For research scientists developing 6G-native sense-communicate-compute-learn systems, you should prioritize addressing the identified open issues of scalability, non-stationarity, and security against poisoning and backdoors. Your focus on communication overhead and real-time safety will be crucial for practical deployments, guiding the development of robust and efficient distributed sensing solutions.

Key insights

MADL unifies decision-making and inference in tightly coupled wireless sensing, communication, and computing systems.

Principles

• 5G-Advanced and 6G visions strengthen integrated sensing and communication.
• Decentralized, partially observed problems are common in advanced wireless.
• Scalability and security are critical open issues for MADL in wireless.

Method

The survey employs a task-driven taxonomy across learning formulations, neural architectures, advanced techniques, and application domains to synthesize MADL research.

In practice

• Apply MADL for MEC offloading with network slicing.
• Utilize federated reinforcement learning for edge intelligence.
• Address security against poisoning in distributed sensor networks.

Topics

• Federated Learning
• Multi-Agent Deep Learning
• Wireless Networks
• Distributed Sensing
• Integrated Sensing and Communication

Best for: Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.