Privacy-Preserving Federated Learning Architecture for Multi-Tenant AI Agent Systems

This document specifies a reference architecture for privacy- preserving federated learning in multi-tenant AI agent deployments. It addresses the challenge of enabling collaborative model training across organizational boundaries while maintaining formal privacy guarantees and tenant data isolation. The architecture combines federated averaging, differential privacy mechanisms, and secure aggregation to enable cross-tenant knowledge transfer without exposing sensitive behavioral data.