论文标题
通过IID和通信感知分组的准确，快速的联合学习
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping
论文作者
论文摘要
联合学习已成为协作机器学习的新范式。但是，它还面临着几个挑战，例如非独立且分布相同的数据（IID）数据和高沟通成本。为此，我们提出了一个新颖的IID和通信感知小组联合学习的框架，该框架通过基于数据分布和节点的物理位置分组节点来同时最大程度地提高准确性和通信速度。此外，我们提供了一种形式的收敛分析和一种称为fedavg-ic的有效优化算法。实验结果表明，与最先进的算法相比，FedAvg-IC提高了测试准确性高达22.2％，并同时将通信时间降低至12％。
Federated learning has emerged as a new paradigm of collaborative machine learning; however, it has also faced several challenges such as non-independent and identically distributed(IID) data and high communication cost. To this end, we propose a novel framework of IID and communication-aware group federated learning that simultaneously maximizes both accuracy and communication speed by grouping nodes based on data distributions and physical locations of the nodes. Furthermore, we provide a formal convergence analysis and an efficient optimization algorithm called FedAvg-IC. Experimental results show that, compared with the state-of-the-art algorithms, FedAvg-IC improved the test accuracy by up to 22.2% and simultaneously reduced the communication time to as small as 12%.
