# A Privacy-Preserving Cloud Architecture for Distributed Machine Learning at Scale

arXiv: https://arxiv.org/abs/2512.10341 · HTML: https://mlsanity.com/paper/2512.10341/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results](https://mlsanity.com/paper/2001.05647/) (2020)
- [Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning](https://mlsanity.com/paper/2103.10847/) (2021)
- [SemDP: Semantic-level Differential Privacy Protection for Face Datasets](https://mlsanity.com/paper/2412.15590/) (2024)

## Built on by (most cited first, 4 of 4)

- [Governing Cloud Data Pipelines with Agentic AI](https://mlsanity.com/paper/2512.23737/) (2025)
- [Scalable Cloud-Native Architectures for Intelligent PMU Data Processing](https://mlsanity.com/paper/2512.22231/) (2025)
- [AI-Driven Cloud Resource Optimization for Multi-Cluster Environments](https://mlsanity.com/paper/2512.24914/) (2025)
- [Cognitive Platform Engineering for Autonomous Cloud Operations](https://mlsanity.com/paper/2601.17542/) (2026)

## Abstract (arXiv)

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.