Results 11 to 20 of about 31,221,148 (284)
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
Yaochu Jin, Hangyu Zhu, Shiqing Liu
exaly +7 more sources
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 +4 more sources
Cross-Domain Federated Data Modeling on Non-IID Data. [PDF]
Comput Intell Neurosci, 2022 Federated learning has received sustained attention in recent years for its distributed training model that fully satisfies the need for privacy concerns. However, under the nonindependent identical distribution, the data heterogeneity of different parties with different data patterns significantly degrades the prediction performance of the federated ...
Chai B, Liu K, Yang R.
europepmc +3 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 ...
Weiwei Wu, Weiwei Wu, Xiumin Wang
exaly +3 more sources
Federated Learning with Non-IID Data [PDF]
CoRR, 2018 Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits.
Yue Zhao 0041 +5 more
openaire +4 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
Federated XGBoost on Sample-Wise Non-IID Data
CoRR, 2022 Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to create and train a model, without exposing the underlying raw data distribution of the local parties involved in the training process.
Katelinh Jones +3 more
openaire +3 more sources
Entropy-Regularized Federated Optimization for Non-IID Data
AlgorithmsFederated learning (FL) struggles under non-IID client data when local models drift toward conflicting optima, impairing global convergence and performance.
Koffka Khan
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Federated multimodal malware classification under non-IID data
CybersecurityMalware data in real-world cybersecurity applications are typically distributed across multiple organizations, and privacy, security, and compliance constraints prevent these data from being shared with a central server.
Shaohua Liu +4 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
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