Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

In the last few years, various communication compression techniques have emerged as an
indispensable tool helping to alleviate the communication bottleneck in distributed learning …

Recent advances in distributed optimization and learning have shown that communication
compression is one of the most effective means of reducing communication. While there …

Federated Learning (FL) has emerged as a promising technique for collaboratively training
machine learning models among multiple participants while preserving privacy-sensitive …

Federated learning (FL) is an emerging machine learning paradigm involving multiple
clients, eg, mobile phone devices, with an incentive to collaborate in solving a machine …

"" Traditionally, to develop these intelligent services and applications, big data are stored
and processed in a centralized model. However, with the proliferation of edge devices and …

在聯邦學習(FL) 中加入更多參與訓練的客戶可以使整個模型學習到更多資料用以改進機器學習
模型. 然而, 傳統聯邦學習設計需要消耗大量的通訊流量, 需要在通訊流量與客戶數量之間取的 …

Communication costs within Federated learning hinder the system scalability for reaching
more data from more clients. The proposed FL adopts a hub-and-spoke network topology …

Communication has been considered as a major bottleneck of Federated Learning (FL) in
mobile edge networks since participating workers iteratively transmit gradients to and …