Federated Learning Architectures for Distributed Multi-Agent Systems: Conceptual Framework and Simulation Analysis

Abstract

The deployment of distributed multi-agent unmanned systems necessitates collaborative learning mechanisms that preserve data privacy, reduce communication overhead, and enable decentralized intelligence. This paper presents a comprehensive conceptual framework for federated learning (FL) architectures tailored to multi-agent robotic systems operating across air, ground, sea, and space domains. We systematically examine the fundamental FL paradigms—horizontal, vertical, and federated transfer learning—and their applicability to heterogeneous agent populations with varying computational capabilities, sensor modalities, and mission objectives. The proposed framework addresses critical challenges including non-IID data distributions arising from diverse operational environments, asynchronous agent participation due to intermittent connectivity, and Byzantine-robust aggregation mechanisms to counter adversarial agents. Through extensive simulation analysis, we evaluate multiple FL architectures including FedAvg, FedProx, and personalized federated learning variants across representative multi-agent scenarios: cooperative target tracking, distributed area coverage, and collaborative manipulation tasks. Performance metrics encompass learning convergence rates, communication efficiency, model accuracy under agent heterogeneity, and resilience to network disruptions. Our simulation results reveal that adaptive aggregation strategies and hierarchical FL topologies significantly outperform centralized approaches in bandwidth-constrained environments while maintaining comparable learning performance. We further investigate the integration of FL with edge computing infrastructure, examining trade-offs between local computation, communication costs, and global model quality. The paper concludes with architectural recommendations for implementing FL in real-world multi-agent systems, identifying open challenges in security, scalability, and real-time adaptation that warrant future research attention.