无政府联邦学习
Anarchic Federated Learning
摘要：
当今部署在边缘网络上的联邦学习（FL）系统由大量的工人组成，这些工人在数据和/或计算能力方面具有高度的异质性，这需要灵活的工人参与时间，努力，数据异质性等。为了满足灵活的工人参与的需要，我们考虑了一种新的FL范式，称为“无政府联邦学习“（AFL）。与传统的FL模型形成鲜明对比的是，AFL中的每个工人都可以自由选择i）何时参与FL，以及ii）基于其当前情况在每一轮中执行的本地步骤的数量（例如，电池电量、通信信道、隐私问题）。然而，AFL中这种混乱的工人行为给算法设计带来了许多新的问题。特别是，目前尚不清楚是否可以开发收敛的AFL训练算法，如果可以，在什么条件下以及可实现的收敛速度有多快。为此，我们提出了两个无政府联邦平均（AFA）算法与双边学习率的跨设备和跨筒仓设置，这是AFA-CD和AFA-CS，分别。有点令人惊讶的是，我们表明，在温和的无政府假设下，AFL算法实现最知名的收敛速度作为国家的最先进的算法，为传统的FL。此外，他们保留了非常理想的{ \em线性加速效果}方面的工人和当地的步骤的数量在新的AFL范例。我们通过对真实世界数据集的广泛实验来验证所提出的算法。
英文摘要：
Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabilities, which call for flexible worker participation in terms of timing, effort, data heterogeneity, etc. To satisfy the need for flexible worker participation, we consider a new FL paradigm called "Anarchic Federated Learning" (AFL) in this paper. In stark contrast to conventional FL models, each worker in AFL has the freedom to choose i) when to participate in FL, and ii) the number of local steps to perform in each round based on its current situation (e.g., battery level, communication channels, privacy concerns). However, such chaotic worker behaviors in AFL impose many new open questions in algorithm design. In particular, it remains unclear whether one could develop convergent AFL training algorithms, and if yes, under what conditions and how fast the achievable convergence speed is. Toward this end, we propose two Anarchic Federated Averaging (AFA) algorithms with two-sided learning rates for both cross-device and cross-silo settings, which are named AFA-CD and AFA-CS, respectively. Somewhat surprisingly, we show that, under mild anarchic assumptions, both AFL algorithms achieve the best known convergence rate as the state-of-the-art algorithms for conventional FL. Moreover, they retain the highly desirable {\em linear speedup effect} with respect of both the number of workers and local steps in the new AFL paradigm. We validate the proposed algorithms with extensive experiments on real-world datasets.
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。
