activity
20172022
most citedEavesdrop the Composition Proportion of Training Labels in Federated Learning

35 citations · 54 across the 5 of their papers we have counts for

collaborators

9 papers

cs.LG20223 cited

Federated Class-Incremental Learning

Jiahua Dong, Lixu Wang, Zhen Fang +4

Federated learning (FL) has attracted growing attention via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume obje…

eess.SY2021

Co-designing Intelligent Control of Building HVACs and Microgrids

Rumia Masburah, Sayan Sinha, Rajib Lochan Jana +2

Building loads consume roughly 40% of the energy produced in developed countries, a significant part of which is invested towards building temperature-control infrastructure. There…

eess.SY20211 cited

Learning-based Framework for Sensor Fault-Tolerant Building HVAC Control with Model-assisted Learning

Shichao Xu, Yangyang Fu, Yixuan Wang +2

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort a…

cs.LG2021

Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator

Shichao Xu, Lixu Wang, Yixuan Wang +1

Data quantity and quality are crucial factors for data-driven learning methods. In some target problem domains, there are not many data samples available, which could significantly…

cs.LG2020

Addressing Class Imbalance in Federated Learning

Lixu Wang, Shichao Xu, Xiao Wang +1

Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution an…

eess.SY2020

One for Many: Transfer Learning for Building HVAC Control

Shichao Xu, Yixuan Wang, Yanzhi Wang +2

The design of building heating, ventilation, and air conditioning (HVAC) system is critically important, as it accounts for around half of building energy consumption and directly…