57 citations · 79 across the 14 of their papers we have counts for
15 papers
PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems
Wei Sun, Yijun Chen, Bo Gao +4
Federated learning (FL) is vulnerable to data poisoning attacks due to its distributed nature. Although recent GAN-based data poisoning methods have indicated the potential of usin…
Recursive Offloading for LLM Serving in Multi-tier Networks
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Heterogeneous device-edge-cloud computing infrastructures have become widely adopted in telecommunication operators and Wide Area Networks (WANs), offering multi-tier computational…
SVAFD: A Secure and Verifiable Co-Aggregation Protocol for Federated Distillation
Tian Wen, Sheng Sun, Yuwei Wang +4
Secure Aggregation (SA) is an indispensable component of Federated Learning (FL) that concentrates on privacy preservation while allowing for robust aggregation. However, most SA d…
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
The rise of End-Edge-Cloud Collaboration (EECC) offers a promising paradigm for Artificial Intelligence (AI) model training across end devices, edge servers, and cloud data centers…
Peak-Controlled Logits Poisoning Attack in Federated Distillation
Yuhan Tang, Aoxu Zhang, Zhiyuan Wu +4
Federated Distillation (FD) offers an innovative approach to distributed machine learning, leveraging knowledge distillation for efficient and flexible cross-device knowledge trans…
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…
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a…
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…
FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
Zhiyuan Wu, Sheng Sun, Yuwei Wang +6
Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data…
Resource-aware Probability-based Collaborative Odor Source Localization Using Multiple UAVs
Shan Wang, Sheng Sun, Min Liu +2
Benefitting from UAVs' characteristics of flexible deployment and controllable movement in 3D space, odor source localization with multiple UAVs has been a hot research area in rec…
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…