9 citations · 17 across the 10 of their papers we have counts for
10 papers
Approximate Wireless Communication for Lossy Gradient Updates in IoT Federated Learning
Xiang Ma, Haijian Sun, Rose Qingyang Hu +1
Federated learning (FL) has emerged as a distributed machine learning (ML) technique that can protect local data privacy for participating clients and improve system efficiency. In…
Exploring Communication Technologies, Standards, and Challenges in Electrified Vehicle Charging
Xiang Ma, Yuan Zhou, Hanwen Zhang +4
As public awareness of environmental protection continues to grow, the trend of integrating more electric vehicles (EVs) into the transportation sector is rising. Unlike convention…
Precise Coil Alignment for Dynamic Wireless Charging of Electric Vehicles with RFID Sensing
Haijian Sun, Xiang Ma, Rose Qingyang Hu +1
Electric vehicle (EV) has emerged as a transformative force for the sustainable and environmentally friendly future. To alleviate range anxiety caused by battery and charging facil…
CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning
Xiang Ma, Qun Wang, Haijian Sun +2
Asynchronous federated learning aims to solve the straggler problem in heterogeneous environments, i.e., clients have small computational capacities that could cause aggregation de…
Approximate Wireless Communication for Federated Learning
Xiang Ma, Haijian Sun, Rose Qingyang Hu +1
This paper presents an approximate wireless communication scheme for federated learning (FL) model aggregation in the uplink transmission. We consider a realistic channel that reve…
A New Implementation of Federated Learning for Privacy and Security Enhancement
Xiang Ma, Haijian Sun, Rose Qingyang Hu +1
Motivated by the ever-increasing concerns on personal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learni…
User Scheduling for Federated Learning Through Over-the-Air Computation
Xiang Ma, Haijian Sun, Qun Wang +1
A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process.…
Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC
Xiang Ma, Haijian Sun, Rose Qingyang Hu
Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive…
Towards Green Mobile Edge Computing Offloading Systems with Security Enhancement
Haijian Sun, Qun Wang, Xiang Ma +2
Mobile edge computing (MEC) is an emerging communication scheme that aims at reducing latency. In this paper, we investigate a green MEC system under the existence of an eavesdropp…
Adaptive Federated Learning With Gradient Compression in Uplink NOMA
Haijian Sun, Xiang Ma, Rose Qingyang Hu
Federated learning (FL) is an emerging machine learning technique that aggregates model attributes from a large number of distributed devices. Several unique features such as energ…