Results 261 to 270 of about 9,950,038 (297)

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, Yiqiang Chen, Xiaodong Yang, Hanchao Yu, Yi-Hua Huang, Yang Gu   +5 more
semanticscholar   +1 more source

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, Mugen Peng   +5 more
semanticscholar   +1 more source

IOFL: Intelligent-Optimization-Based Federated Learning for Non-IID Data

IEEE Internet of Things Journal
Federated learning (FL) algorithm has been widely studied in recent years due to its ability for sharing data while protecting privacy. However, FL has risks, such as model inversion attack, and is less effective when data is nonindependent and ...
Xinyan Li, Huimin Zhao, Wu Deng
semanticscholar   +1 more source

Personalized Federated Graph Learning on Non-IID Electronic Health Records

IEEE Transactions on Neural Networks and Learning Systems
Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns from
Tao Tang, Zhuoyang Han, Zhen Cai, Shuo Yu, Xiaokang Zhou, Taiwo Oseni, Sajal k. Das   +6 more
semanticscholar   +1 more source

ProFed: a Benchmark for Proximity-based non-IID Federated Learning

arXiv.org
In recent years, cro:flFederated learning (FL) has gained significant attention within the machine learning community. Although various FL algorithms have been proposed in the literature, their performance often degrades when data across clients is non ...
D. Domini, G. Aguzzi, M. Viroli
semanticscholar   +1 more source

Digital Twin-Empowered Federated Incremental Learning for Non-IID Privacy Data

IEEE Transactions on Mobile Computing
Federated learning (FL) has emerged as a compelling distributed learning paradigm without sharing local original data. However, with ubiquitous non-independent and identically distributed (non-IID) privacy data, the FL suffers from severe performance ...
Qian Wang, Siguang Chen, Meng Wu, Xue Li
semanticscholar   +1 more source

A Thorough Assessment of the Non-IID Data Impact in Federated Learning

Journal of Industrial Information Integration
Federated learning (FL) allows collaborative machine learning (ML) model training among decentralized clients'information, ensuring data privacy. The decentralized nature of FL deals with non-independent and identically distributed (non-IID) data.
Daniel Mauricio Jimenez Gutierrez, Mehrdad Hassanzadeh, Aris Anagnostopoulos, Ioannis Chatzigiannakis, Andrea Vitaletti   +4 more
semanticscholar   +1 more source

Age-of-Information Minimization in Federated Learning Based Networks With Non-IID Dataset

IEEE Transactions on Wireless Communications
In this paper, a federated learning (FL) based system is investigated with non-independent and identically distributed (non-IID) dataset, where multiple devices participate in the global model aggregation through a limited number of sub-channels.
Kaidi Wang, Zhiguo Ding, Daniel K. C. So, Zhiguo Ding   +3 more
semanticscholar   +1 more source

Privacy-Preserving Federated Learning Against Label-Flipping Attacks on Non-IID Data

IEEE Internet of Things Journal
Federated learning (FL) has attracted widespread attention in the Internet of Things domain recently. With FL, multiple distributed devices can cooperatively train a global model by transmitting model updates without disclosing the original data. However,
Xicong Shen, Ying Liu, Fu Li, Chunguang Li   +3 more
semanticscholar   +1 more source

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