Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
A comprehensive survey of federated transfer learning: challenges, methods and applications
Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …
participants to collaboratively train a centralized model with privacy preservation by …
Fedas: Bridging inconsistency in personalized federated learning
Abstract Personalized Federated Learning (PFL) is primarily designed to provide
customized models for each client to better fit the non-iid distributed client data which is a …
customized models for each client to better fit the non-iid distributed client data which is a …
Federated learning in computer vision
Federated Learning (FL) has recently emerged as a novel machine learning paradigm
allowing to preserve privacy and to account for the distributed nature of the learning process …
allowing to preserve privacy and to account for the distributed nature of the learning process …
Improving global generalization and local personalization for federated learning
Federated learning aims to facilitate collaborative training among multiple clients with data
heterogeneity in a privacy-preserving manner, which either generates the generalized …
heterogeneity in a privacy-preserving manner, which either generates the generalized …
Balancing similarity and complementarity for federated learning
In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively
using data while maintaining user privacy. One key challenge in FL is managing statistical …
using data while maintaining user privacy. One key challenge in FL is managing statistical …
Openfgl: A comprehensive benchmarks for federated graph learning
Federated graph learning (FGL) has emerged as a promising distributed training paradigm
for graph neural networks across multiple local systems without direct data sharing. This …
for graph neural networks across multiple local systems without direct data sharing. This …
FedCPG: A class prototype guided personalized lightweight federated learning framework for cross-factory fault detection
Industrial equipment condition monitoring and fault detection are crucial to ensure the
reliability of industrial production. Recently, data-driven fault detection methods have …
reliability of industrial production. Recently, data-driven fault detection methods have …
Faster stochastic variance reduction methods for compositional minimax optimization
This paper delves into the realm of stochastic optimization for compositional minimax
optimization—a pivotal challenge across various machine learning domains, including deep …
optimization—a pivotal challenge across various machine learning domains, including deep …
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization
Federated Domain Generalization (FedDG) aims to train the global model for generalization
ability to unseen domains with multi-domain training samples. However, clients in federated …
ability to unseen domains with multi-domain training samples. However, clients in federated …
How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?
Federated Adversarial Learning (FAL) is a robust framework for resisting adversarial attacks
on federated learning. Although some FAL studies have developed efficient algorithms, they …
on federated learning. Although some FAL studies have developed efficient algorithms, they …