47 citations · 140 across the 12 of their papers we have counts for
4 papers · 1 filter
Auxo: Efficient Federated Learning via Scalable Client Clustering
Jiachen Liu, Fan Lai, Yinwei Dai +3
Federated learning (FL) is an emerging machine learning (ML) paradigm that enables heterogeneous edge devices to collaboratively train ML models without revealing their raw data to…
Coverage-centric Coreset Selection for High Pruning Rates
Haizhong Zheng, Rui Liu, Fan Lai +1
One-shot coreset selection aims to select a representative subset of the training data, given a pruning rate, that can later be used to train future models while retaining high acc…
Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs
Sanjay Sri Vallabh Singapuram, Fan Lai, Chuheng Hu +1
The need to train DNN models on end-user devices (e.g., smartphones) is increasing with the need to improve data privacy and reduce communication overheads. Unlike datacenter serve…
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing
Yiding Wang, Decang Sun, Kai Chen +2
Training deep neural networks (DNNs) is time-consuming. While most existing solutions try to overlap/schedule computation and communication for efficient training, this paper goes…