组合联邦学习：在分布鲁棒平均与元学习中的应用
Compositional federated learning: Applications in distributionally robust averaging and meta learning
AI总结：
本文提出ComFedL算法解决组合联邦学习框架，通过将分布鲁棒FL和MAML等极小极大优化问题转化为组合优化问题，并证明其$O(1/\sqrt{T})$的收敛速率。
摘要：
在本文中，我们提出了一种有效且高效的组合联邦学习（ComFedL）算法，用于解决一种新的组合联邦学习（FL）框架，该框架频繁出现在许多具有分层结构的数据挖掘和机器学习问题中，例如分布鲁棒FL和模型无关元学习（MAML）。此外，我们在一些温和条件下研究了ComFedL算法的收敛性分析，并证明其实现了$O(\frac{1}{\sqrt{T}})$的收敛速率，其中$T$表示迭代次数。据我们所知，我们的新组合FL框架是首个将联邦学习与组合随机优化相连接的工作。具体而言，我们首先利用KL散度正则化将分布鲁棒FL（即极小极大优化问题）转化为简单的组合优化问题。同时，我们也首次将分布无关的MAML问题（即极小极大优化问题）转化为简单而有效的组合优化问题。最后，我们应用两个流行的机器学习任务，即分布鲁棒FL和MAML，来证明我们算法的有效性。
英文摘要：
In the paper, we propose an effective and efficient Compositional Federated Learning (ComFedL) algorithm for solving a new compositional Federated Learning (FL) framework, which frequently appears in many data mining and machine learning problems with a hierarchical structure such as distributionally robust FL and model-agnostic meta learning (MAML). Moreover, we study the convergence analysis of our ComFedL algorithm under some mild conditions, and prove that it achieves a convergence rate of $O(\frac{1}{\sqrt{T}})$, where $T$ denotes the number of iteration. To the best of our knowledge, our new Compositional FL framework is the first work to bridge federated learning with composition stochastic optimization. In particular, we first transform the distributionally robust FL (i.e., a minimax optimization problem) into a simple composition optimization problem by using KL divergence regularization. At the same time, we also first transform the distribution-agnostic MAML problem (i.e., a minimax optimization problem) into a simple yet effective composition optimization problem. Finally, we apply two popular machine learning tasks, i.e., distributionally robust FL and MAML to demonstrate the effectiveness of our algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
