63 citations · 183 across the 24 of their papers we have counts for
14 papers · 1 filter
Robust Federated Learning against Noisy Clients via Masked Optimization
Xuefeng Jiang, Tian Wen, Zhiqin Yang +5
In recent years, federated learning (FL) has made significant advance in privacy-sensitive applications. However, it can be hard to ensure that FL participants provide well-annotat…
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…
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach
Qingxiang Liu, Sheng Sun, Yuxuan Liang +2
The existing federated learning (FL) methods for spatio-temporal forecasting fail to capture the inherent spatio-temporal heterogeneity, which calls for personalized FL (PFL) metho…
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…
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…
Improving Communication Efficiency of Federated Distillation via Accumulating Local Updates
Zhiyuan Wu, Sheng Sun, Yuwei Wang +3
As an emerging federated learning paradigm, federated distillation enables communication-efficient model training by transmitting only small-scale knowledge during the learning pro…
Federated Skewed Label Learning with Logits Fusion
Yuwei Wang, Runhan Li, Hao Tan +5
Federated learning (FL) aims to collaboratively train a shared model across multiple clients without transmitting their local data. Data heterogeneity is a critical challenge in re…
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
Qingxiang Liu, Sheng Sun, Min Liu +2
Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems (ITS). To mitigate communication burden and tackle with the problem…
Knowledge Distillation in Federated Edge Learning: A Survey
Zhiyuan Wu, Sheng Sun, Yuwei Wang +4
The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL)…
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
Zhiyuan Wu, Sheng Sun, Yuwei Wang +4
The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Compu…
Towards Federated Learning against Noisy Labels via Local Self-Regularization
Xuefeng Jiang, Sheng Sun, Yuwei Wang +1
Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, since high-quality labe…
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from clients without assembling their p…