论文标题
通过组合多臂匪徒精确而快速的联合学习
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits
论文作者
论文摘要
联邦学习已成为协作机器学习的创新范式。与传统的机器学习不同，全局模型是协作学习的，而数据仍在大量客户端设备上分发，因此不会损害用户隐私。然而，尽管受到了广泛的欢迎，仍然存在一些挑战。最重要的是，联邦学习中的全球聚集涉及偏见模型平均和缺乏客户抽样知识的挑战，这反过来又导致高概括误差和缓慢的收敛率。在这项工作中，我们提出了一种称为FedCM的新型算法，该算法通过使用基于多臂强盗的客户采样和使用组合模型平均的多臂bandit客户采样和过滤有偏见的模型来解决这两个挑战。基于使用各种算法和代表性异质数据集进行的广泛评估，我们表明FedCM的表现分别优于最先进的算法，分别以概括性准确性和融合率分别高达37.25％和4.17倍。
Federated learning has emerged as an innovative paradigm of collaborative machine learning. Unlike conventional machine learning, a global model is collaboratively learned while data remains distributed over a tremendous number of client devices, thus not compromising user privacy. However, several challenges still remain despite its glowing popularity; above all, the global aggregation in federated learning involves the challenge of biased model averaging and lack of prior knowledge in client sampling, which, in turn, leads to high generalization error and slow convergence rate, respectively. In this work, we propose a novel algorithm called FedCM that addresses the two challenges by utilizing prior knowledge with multi-armed bandit based client sampling and filtering biased models with combinatorial model averaging. Based on extensive evaluations using various algorithms and representative heterogeneous datasets, we showed that FedCM significantly outperformed the state-of-the-art algorithms by up to 37.25% and 4.17 times, respectively, in terms of generalization accuracy and convergence rate.
