73 citations · 106 across the 9 of their papers we have counts for
10 papers
Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
Yu Jiang, Xindi Tong, Ziyao Liu +3
Federated unlearning has become an attractive approach to address privacy concerns in collaborative machine learning, for situations when sensitive data is remembered by AI models…
Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning
Bokeng Zheng, Bo Rao, Tianxiang Zhu +5
Advances in artificial intelligence (AI) including foundation models (FMs), are increasingly transforming human society, with smart city driving the evolution of urban living.Meanw…
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence
Liekang Zeng, Shengyuan Ye, Xu Chen +6
Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscel…
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Weijie Liu, Xiaoxi Zhang, Jingpu Duan +3
Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data. While prior works…
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
Zhiying Feng, Xu Chen, Qiong Wu +3
Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. M…
A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones
Liekang Zeng, Haowei Chen, Daipeng Feng +2
Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones. Recent research starts to use Deep Neural Networks to enhance drone nav…
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Liekang Zeng, Xu Chen, Peng Huang +3
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures.…
Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets
Huirong Ma, Zhi Zhou, Xiaoxi Zhang +1
Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among…
Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm
Yihong Li, Xiaoxi Zhang, Tianyu Zeng +4
Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each ta…
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Qiong Wu, Xu Chen, Tao Ouyang +4
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for ma…