FedBnR: Mitigating federated learning Non-IID problem by breaking the skewed task and reconstructing representation-Reference-Cited by-同舟云学术

FedBnR: Mitigating federated learning Non-IID problem by breaking the skewed task and reconstructing representation

Published:2024-04 Issue: Volume:153 Page:1-11
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Wang Chao,Xia Hui,Xu Shuo,Chi Hao,Zhang Rui,Hu Chunqiang

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference52 articles.

1. Distributed query processing in the edge-assisted IoT data monitoring system;Cai;IEEE Internet Things J.,2020

2. Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model;Movassagh;J. Ambient Intell. Humaniz. Comput.,2021

3. From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where;Ahmed;IEEE Trans. Ind. Inform.,2022

4. Services management and distributed multihop requests routing in mobile edge networks;Shi;IEEE/ACM Trans. Netw.,2022

5. The eu general data protection regulation (gdpr);Voigt,2017

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