Results 91 to 100 of about 33,062,051 (187)

Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-iid) Data. [PDF]

IEEE Trans Pattern Anal Mach Intell, 2023
Nguyen KP, Treacher AH, Montillo AA.
europepmc   +1 more source

CHPFL: Clustered adaptive hierarchical federated learning for edge-level personalization

High-Confidence Computing
Federated learning faces challenges with non-IID data distributions, often resulting in suboptimal performance for individual clients with the global model. To address this issue, we propose a clustered hierarchical personalized federated learning (CHPFL)
Lihua Song, Jing Li, Honglu Jiang, Shuhua Wei, Yufei Guo   +4 more
doaj   +1 more source

Analyzing the Impact of Non-IID Data on IoT-Enabled Federated Learning for ECG Arrhythmia Detection

The integration of Federated Learning (FL) in the Internet of Medical Things (IoMT) represents a cutting-edge solution, enabling the training of Artificial Intelligence (AI) models directly on edge devices without the need to share sensitive patient ...
Massimo De Vittorio, Gianluigi Semeraro, Davide Cantoro, Angela-Tafadzwa Shumba, Teodoro Montanaro, Ilaria Sergi, Luigi Patrono   +6 more
core   +1 more source

Edge-Federated Learning-Based Intelligent Intrusion Detection System for Heterogeneous Internet of Things

IEEE Access
Distributed denial of service (DDoS) is an awful cyber threat, becoming more prevalent with mature heterogeneous IoT (HetIoT) applications like intelligent agriculture, wearables, and self-driving cars. Developing intelligent intrusion detection systems (
Shalaka S. Mahadik, Pranav M. Pawar, Raja Muthalagu   +2 more
doaj   +1 more source

Global Convergence of Continual Learning on Non-IID Data

CoRR
Continual learning, which aims to learn multiple tasks sequentially, has gained extensive attention. However, most existing work focuses on empirical studies, and the theoretical aspect remains under-explored. Recently, a few investigations have considered the theory of continual learning only for linear regressions, establishes the results based on ...
Fei Zhu 0004, Yujing Liu, Wenzhuo Liu, Zhaoxiang Zhang 0001   +3 more
openaire   +3 more sources

VINO_EffiFedAV: VINO with efficient federated learning through selective client updates for real-time autonomous vehicle object detection

Results in Engineering
The advancement of autonomous vehicle technology relies heavily on sophisticated machine-learning models that facilitate real-time object detection and classification.
K. Vinoth, P. Sasikumar
doaj   +1 more source

Fairness amidst non‐IID graph data: A literature review

AI Magazine
Abstract The growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the underlying data are independent and identically distributed (IID).
Wenbin Zhang 0002, Jeremy C. Weiss, Shuigeng Zhou, Toby Walsh   +3 more
openaire   +3 more sources

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. This open problem has notable consequences, such as decreased model performance and more significant ...
Daniel Mauricio Jimenez Gutierrez, Mehrdad Hassanzadeh, Aris Anagnostopoulos, Ioannis Chatzigiannakis, Andrea Vitaletti   +4 more
openaire   +5 more sources

Federated Learning for Breast Cancer Classification: A Comparative Study of Aggregation Methods

Information
Federated Learning (FL) allows healthcare institutions to collaboratively develop machine learning models while safeguarding patient data, making it ideal for privacy-sensitive medical imaging.
Nadjat Saàdia Lachemi, Medjeded Merati, Saïd Mahmoudi   +2 more
doaj   +1 more source

Differentially Private Federated Clustering Over Non-IID Data

IEEE Internet of Things Journal
34 pages, 4 figures, 1 ...
Yiwei Li 0003, Shuai Wang 0033, Chong-Yung Chi, Tony Q. S. Quek   +3 more
openaire   +2 more sources