PRDO：降噪与偏差校正的隐私去中心化优化方法
Private Decentralized Optimization with Noise Reduction and Bias Correction
做分布式训练又怕隐私噪声影响效果的话，这篇给出了用同批次梯度差分降噪的具体方案，还附了收敛性证明和实验对比。
arXiv 论文提出 Private Recursive Decentralized Optimization（PRDO），针对隐私去中心化学习中的采样噪声、隐私噪声和数据异构导致的去中心化偏差三个问题。PRDO 用递归估计配合同批次梯度差分来降低采样与隐私噪声造成的估计误差，其 Exact Diffusion 组件校正数据异构带来的偏差。理论分析给出了不要求各节点数据异构性一致有界的非凸收敛界，并给出递归梯度差分严格低于私有 Exact Diffusion 查询敏感度的充分条件及满足该条件的示例。实验显示 PRDO 在准确率上优于所评估的基线方法。
Private Decentralized Optimization with Noise Reduction and Bias Correction
Private decentralized learning is affected by sampling noise, privacy noise, and decentralized bias under heterogeneous data. We propose Private Recursive Decentralized Optimization (PRDO). PRDO uses recursive estimation with same-batch gradient differences to reduce estimation errors caused by sampling and privacy noise, while its Exact Diffusion component corrects decentralized bias arising from data heterogeneity. Our analysis establishes a nonconvex convergence bound without assuming uniformly bounded data heterogeneity across nodes. It further gives a sufficient condition under which recursive gradient differences yield strictly lower query sensitivity than private Exact Diffusion, together with an example that rigorously satisfies this condition. Experiments show improved accuracy over the evaluated baselines.