Federated AI for Cross-Cloud Privacy-Compliant Learning Systems

Abstract

Federated AI has emerged as a transformative paradigm for enabling collaborative machine learning across distributed data silos without requiring raw data exchange. In cross-cloud environments, where data custodians operate on heterogeneous cloud platforms with varying privacy regulations and compliance requirements, traditional centralized AI approaches risk data breaches and regulatory non‑compliance. This manuscript proposes a federated AI framework tailored for cross‑cloud privacy‑compliant learning systems, integrating secure aggregation protocols, differential privacy guarantees, and dynamic trust management. The design incorporates a modular architecture comprising local model training, encrypted parameter exchange, and a cloud‑agnostic orchestration layer. We evaluate the framework through two real‑world case studies—healthcare diagnostic imaging across three major cloud providers and financial risk modeling across multinational banking platforms. Results demonstrate that our approach achieves model accuracy within 2% of centralized baselines while reducing privacy leakage metrics by over 60%. We further analyze communication overhead, convergence rates under heterogeneous data distributions, and compliance auditing capabilities. Key contributions include a cross‑cloud encryption scheme, an adaptive privacy budget allocator, and a standardized compliance reporting module. This work advances the state of the art in federated AI by addressing the unique challenges of cross‑cloud deployment and offering a blueprint for privacy‑compliant, high‑performance collaborative learning.