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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
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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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
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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
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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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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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FedProc: Prototypical contrastive federated learning on non-IID data
Future Generation Computer Systems, 2023 Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally. However, when the local data of all clients are not independent and identically distributed (i.e., non-IID), it is challenging to implement this form of efficient collaborative learning.
Xutong Mu, Yulong Shen, Ke Cheng
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Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data. [PDF]
Sensors (Basel), 2023 Seol M, Kim T.
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Federated Learning Architecture for Non-IID Data [PDF]
Jisuanji gongcheng, 2023 In the scenarios of federated learning involving ultra-large-scale edge devices, the local data of participants are non-Independent Identically Distribution(non-IID) pattern, resulting in an imbalance in overall training data and difficulty in defending ...
Tianchen QIU, Xiaoying ZHENG, Yongxin ZHU, Songlin FENG
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