FedProb: An Aggregation Method Based on Feature Probability Distribution for Federated Learning on Non-IID Data-Reference-Cited by-同舟云学术

FedProb: An Aggregation Method Based on Feature Probability Distribution for Federated Learning on Non-IID Data

Published:2022-12-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Big Data (Big Data)
language:
Short-container-title:

Author:

Nguyen Do-Van¹,Tran Anh-Khoa¹,Zettsu Koji¹

Affiliation:

1. National Institute of Information and Communications Technology (NICT),Big Data Integration Research Center,Tokyo,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020923.pdf?arnumber=10020923

Reference20 articles.

1. Federated Learning for Data Privacy Preservation in Vehicular Cyber-Physical Systems

2. Federated optimization in heterogeneous networks;li,0

3. Reading digits in natural images with unsupervised feature learning;netzer;NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning,2011

4. Communication-efficient learning of deep networks from decentralized data;mcmahan;Proceedings of the 20th International Conference on Artificial Intelligence and Statistics AISTATS 2017 20-22 April 2017 Fort Lauderdale FL USA volume 54 of Proceedings of Machine Learning Research,2017

5. Federated learning on non-IID data: A survey

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Selective Updates and Adaptive Masking for Communication-Efficient Federated Learning;IEEE Transactions on Green Communications and Networking;2024-06

2. Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study;Applied Sciences;2024-03-24

3. FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks;2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC);2024-01-08