Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deploy- ment 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. Ex- perimental evaluation across multi-institution workloads shows that the architecture maintains utility with minimal overhead while providing continuous, risk-aware governance. The pro- posed framework establishes a practical foundation for deploying trustworthy and compliant distributed machine learning systems at scale.

翻译：分布式机器学习系统需要强大的隐私保障、可验证的合规性以及在异构多云环境中的可扩展部署。本研究提出一种云原生隐私保护架构，该架构集成了联邦学习、差分隐私、零知识合规证明以及由强化学习驱动的自适应治理机制。该框架支持在不集中敏感数据的情况下进行安全模型训练与推理，同时实现跨机构和云平台的密码学可验证策略执行。部署于混合Kubernetes集群的完整原型系统表明，该架构能有效降低成员推断攻击风险，保持形式化隐私预算的一致性执行，并在差分隐私条件下维持稳定的模型性能。跨多机构工作负载的实验评估显示，该架构能以最小开销保持模型效用，同时提供持续的风险感知治理。所提出的框架为大规模部署可信且合规的分布式机器学习系统奠定了实践基础。