471 citations · 2.2k across the 114 of their papers we have counts for
10 papers · 1 filter
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization
Pengchao Han, Shiqiang Wang, Yang Jiao +1
While providing machine learning model as a service to process users' inference requests, online applications can periodically upgrade the model utilizing newly collected data. Fed…
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
Pengchao Han, Xingyan Shi, Jianwei Huang
Knowledge distillation (KD) can enable collaborative learning among distributed clients that have different model architectures and do not share their local data and model paramete…
Information Elicitation from Decentralized Crowd Without Verification
Kexin Chen, Chao Huang, Jianwei Huang
Information Elicitation Without Verification (IEWV) refers to the problem of eliciting high-accuracy solutions from crowd members when the ground truth is unverifiable. A high-accu…
Federated Learning in Competitive EV Charging Market
Chenxi Sun, Chao Huang, Biying Shou +1
Federated Learning (FL) has demonstrated a significant potential to improve the quality of service (QoS) of EV charging stations. While existing studies have primarily focused on d…
Incentive Mechanism Design for Distributed Ensemble Learning
Chao Huang, Pengchao Han, Jianwei Huang
Distributed ensemble learning (DEL) involves training multiple models at distributed learners, and then combining their predictions to improve performance. Existing related studies…
Information Disclosure under Competition in Sharing Systems
Ningning Ding, Zhixuan Fang, Jianwei Huang
Sharing systems have facilitated the redistribution of underused resources by providing convenient online marketplaces for individual sellers and buyers. However, sellers in these…
Towards Carbon Transparency: A High-Resolution Carbon Emissions Database for China's Listed Companies
Xinlei Wang, Junhua Zhao, Haifeng Wu +7
The dual-carbon goals of China necessitate precise accounting of company carbon emissions, vital for green development across all industries. Not only the company itself but also f…
Joint Participation Incentive and Network Pricing Design for Federated Learning
Ningning Ding, Lin Gao, Jianwei Huang
Federated learning protects users' data privacy through sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine…
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation
Bing Luo, Yutong Feng, Shiqiang Wang +2
Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogene…
Optimization Design for Federated Learning in Heterogeneous 6G Networks
Bing Luo, Xiaomin Ouyang, Peng Sun +3
With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still a…