… ; to further reduce the resource demands of federated training, we envision that the approach
of … architecture into consideration during federated training, is a promising future direction. …

… problems is federated learning (FL), which enables the devices to train a common machine
learning … This paper provides a comprehensive overview of FL applications for envisioned …

… This setting allows the training data to be dispersed in … of federated learning systems, with
a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications…

… Federated learning [1, 2] is a popular distributed learning framework developed for edge
devices. It allows the private data to stay locally while leveraging large-scale computation from …

… the opportunities and challenges in federated learning. … is similar to the traditional Machine
Learning, where we want to … an extension to the vertical federated learning - If we want to …

… is a novel concept that can serve as a solution to the privacy and security issues … its current
challenges followed by the potential of Federated Learning in addressing those challenges. A …

… learning. Finally, we summarize the characteristics of existing federated learning, and
analyze the current practical application of federated learning. …

… This TTF consists of FL API and Federated Core (FC) API. In detail, FL API offers a set of …
learning method to process federated training. FC API, the basic layer for federation learning, …

… challenges in Federated Learning We present three key challenges faced under a Federated
Learning (FL) setting, specifically focusing on challenges arising in cross-device learning. …

… To overcome this challenge, federated learning (FL) appeared to be a promising learning …
survey on FL for big data services and applications is yet to be conducted. In this article, we …