Federated Agentic Intelligence: Integrating Multi-Agent Systems with Decentralized Foundation Models
Abstract
Autonomous agent populations increasingly operate across organizational and geographic boundaries -- enterprise knowledge workers, vehicle fleets, factory robots, and personal device assistants -- generating experience that could improve a shared foundation model but that cannot be centralized due to privacy, bandwidth, or data-sovereignty constraints. This paper presents Federated Agentic Intelligence (FAI), an architecture that integrates multi-agent coordination with federated learning of a shared decentralized foundation model, allowing autonomous agents to both act collaboratively on multi-step tasks and continuously improve a common backbone from locally generated experience without exchanging raw data. FAI combines hierarchical multi-agent consensus, staleness-aware asynchronous federated aggregation, shared episodic memory, and reputation-weighted Byzantine-robust aggregation to handle the combined challenges of distributed decision-making and distributed learning under unreliable, heterogeneous, and occasionally adversarial participants. We evaluate FAI across simulated agent populations spanning enterprise, vehicular, industrial, and personal-device deployments. Our results show that federated hierarchical consensus improves multi-step task success rate by 13.9 percentage points over a centralized-planner baseline and by 27.4 points over uncoordinated independent agents, that staleness-aware asynchronous aggregation preserves 70.2% decision quality at 32 rounds of update staleness versus 44.2% for synchronous aggregation, and that reputation-weighted Byzantine-robust consensus sustains 63.5% task success with 30% adversarial agents present versus a collapse to 14.6% for undefended coordination. We conclude with a discussion of open challenges in cross-agent credit assignment, emergent multi-agent behavior under federated learning dynamics, and the governance of autonomous agent populations that jointly act and learn without centralized oversight.
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