Modern enterprises are trapped in "Data Silos." Due to GDPR and security, AI agents often hit a wall: they can't access the data they need to work. This talk introduces Federated Multi-Agent Systems (FMAS), an architecture where AI Agents coordinate across silos without ever centralizing sensitive information.

Framed through the (slightly chaotic) lens of the Magical Institute for Commons and Energy (MICE), we’ll move beyond theory to demonstrate how to build collaborative, privacy-first AI that satisfies even the most "jealous" data owners.

Target Audience: Data Scientists, AI & ML Engineers
Prerequisites: Basic knowledge about AI models, agents and Python
Level: Advanced

Extended Abstract:
In the modern enterprise, data is rarely in one place. Whether due to GDPR compliance, internal departmental security, or the sheer scale of decentralized systems, we are moving toward a world of "Data Silos." While Large Language Models (LLMs) and Agents promise to automate complex workflows, they often hit a wall: they cannot access or "learn" from data they aren't allowed to see.

This talk explores Federated Multi-Agent Systems (FMAS), an architecture that allows autonomous agents to coordinate and improve their performance without ever centralizing sensitive data. We will move beyond the theory of Federated Learning and look at the practical implementation of "Diplomat Agents" that operate locally and share only high-level insights.

Framed through the (slightly chaotic) lens of a research institute filled with protective academics—the **Magical Institute for Commons and Energy (MICE)**—we will demonstrate how to build a collaborative AI system that respects the privacy of even the most "jealous" data owners.