AI 中文总结
该研究通过理论分析和模拟，区分了联邦Lasso中局部轮次与平均效应，比较多种方法，发现FedDualAvg优化更好但变量恢复不一定更优，轮次效应受多种因素影响。
AI 中文摘要
理论分析和蒙特卡洛模拟将联邦Lasso中的局部轮次效应与平均和调参效应区分开来。在拟合稀疏回归时，平均之前应进行多少局部工作？一个正交计算表明，额外的轮次可能没有影响，而平均仍会扩大所选变量集。一个相关的两站点构造给出了精确的、非单调的极限目标间隙及其最小化轮次数。然后，我们在十二种情景和600次重复中比较了坐标下降平均、两种阈值修改和改编的FedDualAvg。这些方法共享惩罚项、独立验证样本、选择规则和资源限制。FedDualAvg通常实现更小的目标间隙，但并不总是能更好地恢复变量。阈值增益强烈依赖于选择规则。轮次效应随相关性、信号强度、站点分配和所测量的结果而变化。这些结果区分了更快的迭代与更好的优化、预测和变量选择。
英文摘要
Theoretical analysis and Monte Carlo simulation separate local-epoch effects from averaging and tuning effects in federated Lasso. How much local work should precede averaging when fitting a sparse regression? An orthogonal calculation shows that extra epochs can have no effect while averaging still enlarges the selected set. A correlated two-site construction gives an exact, nonmonotone limiting objective gap and its minimizing epoch count. We then compare coordinate-descent averaging, two thresholding modifications, and adapted FedDualAvg across twelve scenarios and 600 replicates. Methods share a penalty, independent validation samples, selection rules, and resource limits. FedDualAvg generally achieves smaller objective gaps but does not always recover variables better. Thresholding gains depend strongly on selection rules. Epoch effects vary with correlation, signal strength, site allocation, and the outcome measured. These results distinguish faster iteration from better optimization, prediction, and variable selection.
