The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and bandwidth optimization problem to minimize the FL training loss. To solve this joint optimization problem, we provide a thorough convergence analysis for TT-Fed. Based on the obtained analytical convergence upper bound, the optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed online search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.
翻译:新兴的联邦学习(FL)框架为以隐私保护的方式培训机器学习模式提供了一种新的方法。然而,传统的FL算法是基于一个事件触发的汇总,它有散装者和通信间接费用问题。为了解决这些问题,我们在本文中提出了一个时间触发的FL算法(TT-Fed),它是一个典型同步和不同步的无线通信通用算法的普遍形式。考虑到无线通信的资源有限和不可靠性质,我们联合研究用户选择和带宽优化问题,以尽量减少FL培训损失。为了解决这一联合优化问题,我们为TTFed提供了彻底的趋同分析分析分析分析。根据分析趋同上限,优化问题被分解为每个全球汇总回合的可移动的子问题,最后通过我们提议的在线搜索算法加以解决。模拟结果显示,与不连续的FL(FedAsync)和FL(FL)相比,将用户级(FedAT-Fed)的精度问题降到最低水平(FII)基准,同时将我们提议的5-CM(C-CRent-Cal-Cal-Cal-Cal-Cal-Lisaliz-Cal-Calislup)分别改进了我们的拟议数据-5-Flus-Flation-Sirlational-Slational-Slationalislationalislislislislislisalisalisalisalisal)。