Search Results for author:
Found 14 papers, 6 papers with code
Privacy-Preserving Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI services without relying on remote servers.
Logits Poisoning Attack in Federated Distillation
Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cross-device knowledge transfer in federated learning.
Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data.
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy.
Federated Skewed Label Learning with Logits Fusion
By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category.
Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks
However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards.
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation.
Knowledge Distillation in Federated Edge Learning: A Survey
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), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data.
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC.
Multi-Granularity Network with Modal Attention for Dense Affective Understanding
Video affective understanding, which aims to predict the evoked expressions by the video content, is desired for video creation and recommendation.
Tunable Doping of Rhenium and Vanadium into Transition Metal Dichalcogenides for Two-Dimensional Electronics
Alternatively, using highly conductive doped TMDCs will have a profound impact on the contact engineering of 2D electronics.
Materials Science
Integrated Communication and Localization in mmWave Systems
As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation mobile communication systems have been conducted.
Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN
Finding a template in a search image is an important task underlying many computer vision applications.
Conformal and Low-Rank Sparse Representation for Image Restoration
In this paper, we propose a novel sparse representation approach called conformal and low-rank sparse representation (CLRSR) for image restoration problems.
