63 citations · 181 across the 21 of their papers we have counts for
7 papers · 1 filter
Learnable Sparse Customization in Heterogeneous Edge Computing
Jingjing Xue, Sheng Sun, Min Liu +3
To effectively manage and utilize massive distributed data at the network edge, Federated Learning (FL) has emerged as a promising edge computing paradigm across data silos. Howeve…
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting
Qingxiang Liu, Sheng Sun, Yuxuan Liang +6
Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of r…
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
Xuefeng Jiang, Sheng Sun, Jia Li +6
Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However,…
FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation
Quyang Pan, Sheng Sun, Zhiyuan Wu +4
Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite…
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
Zhiyuan Wu, Sheng Sun, Yuwei Wang +4
On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI inference without relying on remote serv…
Logits Poisoning Attack in Federated Distillation
Yuhan Tang, Zhiyuan Wu, Bo Gao +3
Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cr…
Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
Zhiyuan Wu, Tianliu He, Sheng Sun +4
Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data. Mainstream FL methodologies overlook the dynamic nature…