隐私增强联邦学习框架通过异步聚合和本地差分扰动提升性能
Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation
朋友间推荐：新提出的隐私框架，在保护数据隐私的同时，还能提升模型训练效率，比FedAvg更省通信。
这项研究提出了一种隐私增强联邦学习框架，整合动态差分隐私、轻量同态加密和本地差分隐私机制来保护数据隐私。实验在CIFAR-10和Purchase-100数据集上验证，在ε=0.1的严格隐私约束下，该方法保持高达82.6%的分类准确率，相比FedAvg减少21.3%的通信开销。
Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation
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.
