Survey of federated learning research by Chuanxin ZHOU, Yi SUN, Degang WANG, Huawei GE

Subjects: “…federated learning…”
Get full text

A Bibliometric Analysis on Federated Learning by Ersin Namlı, Yusuf Sait Türkan, Mesut Ulu, Ömer Algorabi

Subjects: “…federated learning…”
Get full text

Decentralized Federated Learning with Prototype Exchange by Lu Qi, Haoze Chen, Hongliang Zou, Shaohua Chen, Xiaoying Zhang, Hongyan Chen

Subjects: “…federated learning…”
Get full text

Federated Learning in Data Privacy and Security by Dokuru Trisha Reddy, Haripriya Nandigam, Sai Charan Indla, S. P. Raja

“…Federated learning (FL) has been a rapidly growing topic in recent years. …”
Get full text

Survey on Byzantine attacks and defenses in federated learning by ZHAO Xiaojie, SHI Jinqiao, HUANG Mei, KE Zhenhan, SHEN Liyan

Subjects: “…federated learning…”
Get full text

Elastic Optimization for Stragglers in Edge Federated Learning by Khadija Sultana, Khandakar Ahmed, Bruce Gu, Hua Wang

Subjects: Get full text

An incentive mechanism with bandwidth allocation for federated learning by Yingyun GUO, Bo GAO, Zhifei ZHANG, Yu ZHANG, Ke XIONG

Subjects: “…federated learning…”
Get full text

Federated learning assisted distributed energy optimization by Yuhan Du, Nuno Mendes, Simin Rasouli, Javad Mohammadi, Pedro Moura

“…This paper specifically focuses on integrating federated learning (FL) with the multi‐agent energy management procedure. …”
Get full text

Review of communication optimization methods in federated learning by YANG Zhikai, LIU Yaping, ZHANG Shuo, SUN Zhe, YAN Dingyu

Subjects: “…federated learning…”
Get full text

Application of federated learning in predicting breast cancer by Chai Jiarui

“…This paper proposes a breast cancer prediction model combined with federated learning, where each participant trains the model locally using multimodal data such as imaging, genes, and treatment history. …”
Get full text

Federated Learning Based on OPTICS Clustering Optimization by Chenyang Lu, Su Deng, Yahui Wu, Haohao Zhou, Wubin Ma

“…Federated learning (FL) has emerged for solving the problem of data fragmentation and isolation in machine learning based on privacy protection. …”
Get full text

A survey of security threats in federated learning by Yunhao Feng, Yanming Guo, Yinjian Hou, Yulun Wu, Mingrui Lao, Tianyuan Yu, Gang Liu

Subjects: “…Federated learning…”
Get full text

Federated Learning Applications in Fingerprint and Finger Vein Recognition by Wang Yongchao

“…This article summarizes several approaches that combine federated learning with fingerprint and finger vein recognition to solve privacy issues. …”
Get full text

A survey of federated learning for 6G networks by Guanglei GENG, Bo GAO, Ke XIONG, Pingyi FAN, Yang LU, Yuwei WANG

Subjects: Get full text

Adversarial sample generation algorithm for vertical federated learning by Xiaolin CHEN, Daoguang ZAN, Bingchao WU, Bei GUAN, Yongji WANG

“…To adapt to the scenario characteristics of vertical federated learning (VFL) applications regarding high communication cost, fast model iteration, and decentralized data storage, a generalized adversarial sample generation algorithm named VFL-GASG was proposed.Specifically, an adversarial sample generation framework was constructed for the VFL architecture.A white-box adversarial attack in the VFL was implemented by extending the centralized machine learning adversarial sample generation algorithm with different policies such as L-BFGS, FGSM, and C＆amp;W.By introducing deep convolutional generative adversarial network (DCGAN), an adversarial sample generation algorithm named VFL-GASG was designed to address the problem of universality in the generation of adversarial perturbations.Hidden layer vectors were utilized as local prior knowledge to train the adversarial perturbation generation model, and through a series of convolution-deconvolution network layers, finely crafted adversarial perturbations were produced.Experiments show that VFL-GASG can maintain a high attack success while achieving a higher generation efficiency, robustness, and generalization ability than the baseline algorithm, and further verify the impact of relevant settings for adversarial attacks.…”
Get full text

TDMA-based user scheduling policies for federated learning by Meixia TAO, Dong WANG, Rui SUN, Naifu ZHANG

Subjects: “…federated learning…”
Get full text

Federated learning optimization algorithm based on incentive mechanism by Youliang TIAN, Shihong WU, Ta LI, Lindong WANG, Hua ZHOU

Subjects: “…federated learning…”
Get full text

Adaptive clustering federated learning via similarity acceleration by Suxia ZHU, Binke GU, Guanglu SUN

Subjects: “…federated learning…”
Get full text

Survey on vertical federated learning: algorithm, privacy and security by Jinyin CHEN, Rongchang LI, Guohan HUANG, Tao LIU, Haibin ZHENG, Yao CHENG

Subjects: “…vertical federated learning…”
Get full text

A Survey of Differential Privacy Techniques for Federated Learning by Wang Xin, Li Jiaqian, Ding Xueshuang, Zhang Haoji, Sun Lianshan

Subjects: Get full text