March 13, 2024, 4:44 a.m. | Xiaoxue Zhang, Yifan Hua, Chen Qian

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.07079v2 Announce Type: replace-cross
Abstract: Federated Learning (FL) is a well-known paradigm of distributed machine learning on mobile and IoT devices, which preserves data privacy and optimizes communication efficiency. To avoid the single point of failure problem in FL, decentralized federated learning (DFL) has been proposed to use peer-to-peer communication for model aggregation, which has been considered an attractive solution for machine learning tasks on distributed personal devices. However, this process is vulnerable to attackers who share false models and …

abstract aggregation arxiv blockchain communication cs.cr cs.lg data data privacy decentralized devices distributed efficiency failure federated learning iot machine machine learning mobile paradigm peer peer-to-peer privacy type

Senior Data Engineer

@ Displate | Warsaw

Professor/Associate Professor of Health Informatics [LKCMedicine]

@ Nanyang Technological University | NTU Novena Campus, Singapore

Research Fellow (Computer Science (and Engineering)/Electronic Engineering/Applied Mathematics/Perception Sciences)

@ Nanyang Technological University | NTU Main Campus, Singapore

Java Developer - Assistant Manager

@ State Street | Bengaluru, India

Senior Java/Python Developer

@ General Motors | Austin IT Innovation Center North - Austin IT Innovation Center North

Research Associate (Computer Engineering/Computer Science/Electronics Engineering)

@ Nanyang Technological University | NTU Main Campus, Singapore