A Privacy-Preserving Cloud Architecture for Distributed Machine Learning at Scale
This work addresses privacy and compliance issues for institutions deploying distributed machine learning across multi-cloud environments, representing an incremental improvement by combining existing techniques into a unified framework.
The paper tackles the challenge of ensuring privacy, compliance, and scalability in distributed machine learning by introducing a cloud-native architecture that integrates federated learning, differential privacy, and zero-knowledge proofs, resulting in reduced membership-inference risk and stable model performance with minimal overhead.
Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture that integrates federated learning, differential privacy, zero-knowledge compliance proofs, and adaptive governance powered by reinforcement learning. The framework supports secure model training and inference without centralizing sensitive data, while enabling cryptographically verifiable policy enforcement across institutions and cloud platforms. A full prototype deployed across hybrid Kubernetes clusters demonstrates reduced membership-inference risk, consistent enforcement of formal privacy budgets, and stable model performance under differential privacy. Experimental evaluation across multi-institution workloads shows that the architecture maintains utility with minimal overhead while providing continuous, risk-aware governance. The proposed framework establishes a practical foundation for deploying trustworthy and compliant distributed machine learning systems at scale.