发表机构
Georgetown University; LinkedIn; Shanghai Ocean University（乔治城大学; 领英; 上海海洋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种集成动态差分隐私、轻量级同态加密和本地差分隐私的联邦学习框架，采用异步聚合策略，在严格隐私约束下保持高精度并降低通信开销。
AI 中文摘要
本研究提出了一种隐私增强的联邦学习框架，以解决分布式数据环境中的安全协作训练问题。该框架集成了动态差分隐私（DDP）、轻量级同态加密（HE）和本地差分隐私（LDP）机制，以确保模型训练过程中的数据隐私保护。此外，该框架采用带版本控制的异步聚合策略，以支持异步环境中的分布式训练。在CIFAR-10和Purchase-100基准数据集上的实验验证表明，即使在严格的隐私约束（ε = 0.1）下，该方法仍能保持较高的分类准确率（最高达82.6%），同时与FedAvg相比，通信开销降低了21.3%。实验结果表明，该框架在分布式机器学习场景中有效平衡了隐私保护与模型性能，为大规模分布式协作计算提供了可扩展的技术基础。
英文摘要
This study proposes a privacy-enhanced federated learning framework to address secure collaborative training in distributed data environments. The framework integrates Dynamic Differential Privacy (DDP), lightweight Homomorphic Encryption (HE), and Local Differential Privacy (LDP) mechanisms to ensure data privacy protection during model training. Additionally, the framework employs an asynchronous aggregation strategy with version control to support distributed training in asynchronous environments. Experimental validation on the CIFAR-10 and Purchase-100 benchmark datasets demonstrates that the method maintains high classification accuracy (up to 82.6%) even under stringent privacy constraints (ε = 0.1), while reducing communication overhead by 21.3% compared to FedAvg. Experimental results demonstrate that this framework effectively balances privacy protection and model performance in distributed machine learning scenarios, providing a scalable technical foundation for large-scale distributed collaborative computing.
CommentsPublished in Proc. SPIE 14128, Third International Conference on Big Data, Computational Intelligence, and Applications (BDCIA 2025), 141283L. Event: BDCIA 2025, Huanggang, China. https://doi.org/10.1117/12.3106592
Journal refProc. SPIE 14128, Third International Conference on Big Data, Computational Intelligence, and Applications (BDCIA 2025), 141283L (2026)
