Results 11 to 20 of about 7,049,980 (289)
Federated learning on non-IID data: A survey [PDF]
Neurocomputing, 2021 Federated learning is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse performance than those trained in the standard centralized learning mode, especially when the training data are not independent and identically distributed (Non-IID) on the local devices. In
Hangyu Zhu +2 more
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Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting [PDF]
Engineering, 2016 While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services.
Longbing Cao
doaj +4 more sources
A Multiscale Clustering Approach for Non-IID Nominal Data. [PDF]
Comput Intell Neurosci, 2021 Multiscale brings great benefits for people to observe objects or problems from different perspectives. Multiscale clustering has been widely studied in various disciplines. However, most of the research studies are only for the numerical dataset, which is a lack of research on the clustering of nominal dataset, especially the data are nonindependent ...
Chen R, Zhao S, Tian Z.
europepmc +4 more sources
Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing
IEEE Access, 2021 Federated Learning (FL) has recently attracted considerable attention in internet of things, due to its capability of enabling mobile clients to collaboratively learn a global prediction model without sharing their privacy-sensitive data to the server ...
Wenyu Zhang +4 more
doaj +3 more sources
An Optimization Method for Non-IID Federated Learning Based on Deep Reinforcement Learning [PDF]
Sensors, 2023 Federated learning (FL) is a distributed machine learning paradigm that enables a large number of clients to collaboratively train models without sharing data.
Xutao Meng +3 more
doaj +2 more sources
Federated Learning With Taskonomy for Non-IID Data
IEEE Transactions on Neural Networks and Learning Systems, 2023 Classical federated learning approaches incur significant performance degradation in the presence of non-IID client data. A possible direction to address this issue is forming clusters of clients with roughly IID data. Most solutions following this direction are iterative and relatively slow, also prone to convergence issues in discovering underlying ...
Hadi Jamali-Rad, Anuj Singh
exaly +6 more sources
Secure and decentralized federated learning framework with non-IID data based on blockchain [PDF]
HeliyonFederated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data.
Feng Zhang +3 more
doaj +2 more sources
Cloud–Edge–End Collaborative Federated Learning: Enhancing Model Accuracy and Privacy in Non-IID Environments [PDF]
SensorsCloud–edge–end computing architecture is crucial for large-scale edge data processing and analysis. However, the diversity of terminal nodes and task complexity in this architecture often result in non-independent and identically distributed (non-IID ...
Ling Li, Lidong Zhu, Weibang Li
doaj +2 more sources
Coupled Matrix Factorization Within Non-IID Context [PDF]
, 2015 Recommender systems research has experienced different stages such as from user preference understanding to content analysis. Typical recommendation algorithms were built on the following bases: (1) assuming users and items are IID, namely independent and identically distributed, and (2) focusing on specific aspects such as user preferences or contents.
Fangfang Li, Guandong Xu, Longbing Cao
openaire +3 more sources
IEEE AccessFederated Learning is a promising paradigm for sharing Cyber Threat Intelligence (CTI) without privacy issues by leveraging the cross-silos data in Software Defined Networking (SDN).
Syed Hussain Ali Kazmi +4 more
doaj +2 more sources