Speaker
Description
Recent advances in artificial intelligence have enabled autonomous decision systems capable of interpreting complex multimodal data streams. However, many real world environments such as healthcare infrastructures, cyber physical systems, and distributed IoT networks operate under strict privacy and security constraints that prevent centralized data aggregation. Traditional machine learning pipelines rely on centralized training and therefore struggle to operate within these environments.
This study proposes a federated agentic artificial intelligence architecture designed for privacy preserving decision support in distributed environments. The framework combines federated representation learning with a locally deployed reasoning agent capable of interpreting temporal data streams and generating structured intervention strategies. In the proposed system, only compact encoder updates are shared across participating nodes, while high level reasoning and planning modules remain entirely local. This architecture reduces data exposure while maintaining collaborative learning across institutions.
The system follows a perception, memory, forecasting, and action cycle. A temporal encoder processes heterogeneous sensor or clinical signals to estimate risk trajectories over time. Federated aggregation allows multiple institutions or devices to jointly improve the encoder without exchanging raw data. A local agentic controller maintains a compact belief state, evaluates predicted risks, and produces explainable intervention plans using rule grounded reasoning. This design integrates machine learning prediction with interpretable decision generation.
Experimental evaluation using simulated multi client environments demonstrates that federated temporal encoding improves predictive stability while maintaining strict data locality. Compared with centralized baselines, the proposed architecture achieves competitive predictive performance while eliminating the need for raw data sharing. The framework also provides transparent reasoning traces that support human oversight.
The results indicate that agentic federated systems represent a promising direction for privacy sensitive artificial intelligence deployment in healthcare monitoring, distributed cybersecurity platforms, and intelligent infrastructure. By integrating collaborative representation learning with local autonomous reasoning, the architecture enables scalable and trustworthy AI systems for environments where data sharing is limited.