60 citations · 86 across the 8 of their papers we have counts for
10 papers
Server Free Wireless Federated Learning: Architecture, Algorithm, and Analysis
Howard H. Yang, Zihan Chen, Tony Q. S. Quek
We demonstrate that merely analog transmissions and match filtering can realize the function of an edge server in federated learning (FL). Therefore, a network with massively distr…
Federated Stochastic Gradient Descent Begets Self-Induced Momentum
Howard H. Yang, Zuozhu Liu, Yaru Fu +2
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistic…
Revisiting Analog Over-the-Air Machine Learning: The Blessing and Curse of Interference
Howard H. Yang, Zihan Chen, Tony Q. S. Quek +1
We study a distributed machine learning problem carried out by an edge server and multiple agents in a wireless network. The objective is to minimize a global function that is a su…
Age of Information in Random Access Networks: A Spatiotemporal Study
Howard H. Yang, Ahmed Arafa, Tony Q. S. Quek +1
We investigate the age-of-information (AoI) in the context of random access networks, in which transmitters need to send a sequence of information packets to intended receivers ove…
Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
Howard H. Yang, Ahmed Arafa, Tony Q. S. Quek +1
Federated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for…
Scheduling Policies for Federated Learning in Wireless Networks
Howard H. Yang, Zuozhu Liu, Tony Q. S. Quek +1
Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing pri…