隐私对齐联邦学习新方法提升效率
Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication
这是篇关于如何提升联邦学习隐私和效率的论文，方法很新颖，对研究联邦学习或隐私计算的人可能有参考价值。
这篇论文提出了一种隐私对齐的个性化联邦学习方法，通过一次发布私密的客户端上下文，将重复适应限制在固定系数空间内，解决了传统方法中低维度客户端变化与高维度更新之间的结构不匹配问题。该方法在MNIST和CIFAR-10数据集上测试，在保持隐私预算和客户端异质性的同时，将受保护的上传通信量减少了2.67倍。
Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication
Record-level differential privacy exposes a structural misalignment in personalized federated learning when client-specific variation is low-dimensional while training repeatedly releases high-dimensional updates. In this paper, we address this misalignment by releasing a private client context once and confining repeated adaptation to a fixed coefficient space. Beyond dimensionality reduction, the factorized generator induces an adaptive optimization geometry that reshapes noisy updates, and controlled ablations show that most of its private-training gain is retained by radial evolution. To further reduce the communication cost, we realize the Gaussian mechanism for coefficient updates directly through variable-length quantization with finite expected code length, so that the quantization error itself serves as the required privacy perturbation rather than extra distortion. Across MNIST and CIFAR-10, our design matches or outperforms full-model private adaptation across privacy budgets and client heterogeneity, while reducing protected uplink by a factor of 2.67 at \(\varepsilon=16\) on CIFAR-10 with comparable future-client accuracy.
