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 ...
Weiwei Wu, Weiwei Wu, Xiumin Wang
exaly +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
Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting
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
exaly +3 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
Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data [PDF]
Sensors, 2023 Taejoon Kim
exaly +2 more sources
Federated Data Augmentation Algorithm for Non-independent and Identical Distributed Data [PDF]
Jisuanji kexue, 2022 In federated learning,the local data distribution of users changes with the location and preferences of users,the data under the non-independent and identical distributed(Non-IID) data may lack data of some label categories,which significantly affects ...
QU Xiang-mou, WU Ying-bo, JIANG Xiao-ling
doaj +1 more source
Detecting Outliers in Non-IID Data: A Systematic Literature Review
IEEE Access, 2023 Outlier detection (outlier and anomaly are used interchangeably in this review) in non-independent and identically distributed (non-IID) data refers to identifying unusual or unexpected observations in datasets that do not follow an independent and ...
Shafaq Siddiqi +3 more
doaj +1 more source
FedLC: Optimizing Federated Learning in Non-IID Data via Label-Wise Clustering
IEEE Access, 2023 As contemporary systems are being operated in dynamic situations alternating into decentralized and distributed environments from conventional centralized frameworks, Federated Learning (FL) has been gaining attention for an effective architecture when ...
Hunmin Lee, Daehee Seo
doaj +1 more source
Federated Learning Framework for IID and Non-IID datasets of Medical Images
Emitter: International Journal of Engineering Technology, 2023 Advances have been made in the field of Machine Learning showing that it is an effective tool that can be used for solving real world problems. This success is hugely attributed to the availability of accessible data which is not the case for many fields
Kavitha Srinivasan +3 more
doaj +1 more source