构建可验证隐私保护的分布式机器学习云架构，支持跨多云安全训练。
A Privacy-Preserving Cloud Architecture for Distributed Machine Learning at Scale
- 融合联邦学习与差分隐私，实现数据不集中下的安全训练。
- 通过零知识证明验证合规性，隐私预算控制稳定，模型性能无显著下降。
- 适合需要跨机构、多云部署且高合规要求的大型机密场景。
分布式机器学习系统需具备强隐私保障、可验证合规性，并可在异构多云环境中可扩展部署。本文提出一种原生云环境的隐私保护架构，集成联邦学习、差分隐私、零知识合规证明及强化学习驱动的自适应治理机制。该框架支持无需集中敏感数据的安全模型训练与推理，同时可在不同机构和云平台间实现密码学可验证的策略执行。在混合Kubernetes集群上的完整原型验证表明，该架构显著降低成员推断风险，持续维持形式化隐私预算，且在差分隐私下模型性能稳定。多机构工作负载的实验评估显示，系统在保持高模型效用的同时开销极小，实现了持续的风险感知治理。该框架为大规模可信、合规分布式机器学习部署提供了实用基础。
原文摘要 · Abstract (English)
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.
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