58 citations · 160 across the 27 of their papers we have counts for
6 papers · 1 filter
Multi-Job Intelligent Scheduling with Cross-Device Federated Learning
Ji Liu, Juncheng Jia, Beichen Ma +5
Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine le…
SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting Challenge at KDD Cup 2022
Jingbo Zhou, Xinjiang Lu, Yixiong Xiao +4
The variability of wind power supply can present substantial challenges to incorporating wind power into a grid system. Thus, Wind Power Forecasting (WPF) has been widely recognize…
Accelerated Federated Learning with Decoupled Adaptive Optimization
Jiayin Jin, Jiaxiang Ren, Yang Zhou +3
The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients. Recently, many heuri…
Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources
Ji Liu, Daxiang Dong, Xi Wang +5
Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which ex…
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity
Guanghao Li, Yue Hu, Miao Zhang +4
Federated Learning (FL) enables training a global model without sharing the decentralized raw data stored on multiple devices to protect data privacy. Due to the diverse capacity o…
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server
Hong Zhang, Ji Liu, Juncheng Jia +3
Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. In this p…