Emerging intelligent wireless networks increasingly rely on autonomous AI agents to adapt spectrum usage to dynamic and heterogeneous operating conditions. However, collective learning is challenging when agents experience different local interference environments and cannot exchange raw observations due to communication-overhead and privacy constraints. We propose Federated Agentic Spectrum Coordination with Interference Awareness (FASCIA), a federated multi-agent deep reinforcement learning framework for adaptive spectrum coordination. In FASCIA, each network manager acts as an autonomous AI agent that perceives its local interference environment, accumulates experience in a persistent local memory, and learns an adaptive spectrum coordination policy through local interactions. Coordination among agents is achieved through periodic federated aggregation of locally trained models, without exchanging raw sensing data or experience. Since spectrum decisions made by one agent alter the environments observed by others, local learning is inherently heterogeneous and coupled. FASCIA employs proximal regularization to mitigate the resulting model drift and explicitly accounts for inter-carrier interference arising from partially overlapping channels. FASCIA is the first federated agentic AI framework to address this challenging interference-coupled spectrum coordination setting. Extensive simulations demonstrate that FASCIA achieves performance close to the centralized benchmark while avoiding raw-experience exchange and maintaining effectiveness across heterogeneous network configurations.

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