Results 141 to 150 of about 34,864,804 (179)

Ensemble Federated Learning With Non-IID Data in Wireless Networks

IEEE Transactions on Wireless Communications
Federated learning is a promising technique to implement network intelligence for the sixth generation (6G) communication systems. However, the collected data in wireless networks is non-independent and identically distributed (non-IID), which leads to ...
Zhongyuan Zhao, Jingyi Wang, Wei-Ming Hong, Tony Q. S. Quek, Zhiguo Ding, Mu-Gen Peng   +5 more
semanticscholar   +1 more source

FedEL: Federated ensemble learning for non-iid data

Expert Systems with Applications
Federated learning (FL) is a joint training pattern that fully utilizes data information whereas protecting data privacy. A key challenge in FL is statistical heterogeneity, which arises on account of the heterogeneity of local data distributions among clients, leading to inconsistency in local optimization goals and ultimately reducing the performance
Xing Wu 0001, Jie Pei, Xian-Hua Han, Yen-Wei Chen 0001, Junfeng Yao, Yang Liu 0005, Quan Qian, Yike Guo   +7 more
openaire   +3 more sources

Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data

Neural Information Processing Systems
Federated learning has become a pivotal distributed learning paradigm, involving collaborative model updates across multiple nodes with private data. However, handling non-i.i.d.
Xiaohong Chen, Can-Ran Xiao, Yongmei Liu
semanticscholar   +1 more source

Private Data Synthesis from Decentralized Non-IID Data

2023 International Joint Conference on Neural Networks (IJCNN), 2023
Muhammad Usama Saleem, Liyue Fan
openaire   +2 more sources

Communication-Efficient Federated Data Augmentation on Non-IID Data

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022
Hui Wen 0005, Yue Wu, Jingjing Li 0001, Hancong Duan   +3 more
openaire   +2 more sources

Defending Against Data Poisoning Attack in Federated Learning With Non-IID Data

IEEE Transactions on Computational Social Systems
Federated learning (FL) is an emerging paradigm that allows participants to collaboratively train deep learning tasks while protecting the privacy of their local data.
Chunyong Yin, Qingkui Zeng
semanticscholar   +1 more source

A Novel Approach for Federated Learning with Non-IID Data

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2022
Hiep Thi Hong Nguyen, Harikrishna Warrier, Yogesh Gupta   +2 more
openaire   +1 more source

Federated Dictionary Learning from Non-IID Data

2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2022
Alexandros Gkillas, Dimitris Ampeliotis, Kostas Berberidis   +2 more
openaire   +1 more source

Learning Critically: Selective Self-Distillation in Federated Learning on Non-IID Data

IEEE Transactions on Big Data
Federated learning (FL) enables multiple clients to collaboratively train a global model while keeping local data decentralized. Data heterogeneity (non-IID) across clients has imposed significant challenges to FL, which makes local models re-optimize ...
Yuting He, Yi-Qiang Chen, Xiaodong Yang, Han-Chao Yu, Yi-Hua Huang, Yang Gu   +5 more
semanticscholar   +1 more source

Discrepancy-Aware Federated Learning for Non-IID Data

2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023
Jianhua Shen, Siguang Chen
openaire   +1 more source