41 citations · 53 across the 24 of their papers we have counts for
4 papers · 1 filter
High-Energy Concentration for Federated Learning in Frequency Domain
Haozhi Shi, Weiying Xie, Hangyu Ye +4
Federated Learning (FL) presents significant potential for collaborative optimization without data sharing. Since synthetic data is sent to the server, leveraging the popular conce…
Towards Accurate and Efficient Sub-8-Bit Integer Training
Wenjin Guo, Donglai Liu, Weiying Xie +7
Neural network training is a memory- and compute-intensive task. Quantization, which enables low-bitwidth formats in training, can significantly mitigate the workload. To reduce qu…
HeteroTune: Efficient Federated Learning for Large Heterogeneous Models
Ruofan Jia, Weiying Xie, Jie Lei +3
While large pre-trained models have achieved impressive performance across AI tasks, their deployment in privacy-sensitive and distributed environments remains challenging. Federat…
Physics Inspired Criterion for Pruning-Quantization Joint Learning
Weiying Xie, Xiaoyi Fan, Xin Zhang +3
Pruning-quantization joint learning always facilitates the deployment of deep neural networks (DNNs) on resource-constrained edge devices. However, most existing methods do not joi…