7 citations · 28 across the 22 of their papers we have counts for
5 papers · 1 filter
DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices
Guanyu Xu, Zhiwei Hao, Yong Luo +3
Recent years have witnessed the great success of vision transformer (ViT), which has achieved state-of-the-art performance on multiple computer vision benchmarks. However, ViT mode…
Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient
Rongfei Fan, Xuming An, Shiyuan Zuo +1
Over-the-air computation is a communication-efficient solution for federated learning (FL). In such a system, iterative procedure is performed: Local gradient of private loss funct…
Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learning
Xuming An, Rongfei Fan, Shiyuan Zuo +3
Federated learning (FL) has emerged as an appealing machine learning approach to deal with massive raw data generated at multiple mobile devices, {which needs to aggregate the trai…
Joint Task Offloading and Resource Allocation for Streaming Application in Cooperative Mobile Edge Computing
Xiang Li, Rongfei Fan, Han Hu +1
Mobile edge computing (MEC) enables resource-limited IoT devices to complete computation-intensive or delay-sensitive task by offloading the task to adjacent edge server deployed a…
FedABC: Targeting Fair Competition in Personalized Federated Learning
Dui Wang, Li Shen, Yong Luo +4
Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poo…