Results 11 to 20 of about 109,032 (273)
Byzantine-robust federated learning over Non-IID data
Tongxin xuebao, 2023 The malicious attacks of Byzantine nodes in federated learning was studied over the non-independent and identically distributed dataset , and a privacy protection robust gradient aggregation algorithm was proposed.A reference gradient was designed to ...
Xindi MA +6 more
doaj +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
doaj +2 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
Data augmentation scheme for federated learning with non-IID data
Tongxin xuebao, 2023 To solve the problem that the model accuracy remains low when the data are not independent and identically distributed (non-IID) across different clients in federated learning, a privacy-preserving data augmentation scheme was proposed.Firstly, a data ...
Lingtao TANG, Di WANG, Shengyun LIU
doaj +3 more sources
Byzantine-robust federated learning via credibility assessment on non-IID data
Mathematical Biosciences and Engineering, 2022 Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands.
Kun Zhai +3 more
doaj +4 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
Zhu, Hangyu +3 more
openaire +2 more sources
Homophily outlier detection in non-IID categorical data [PDF]
Data Mining and Knowledge Discovery, 2021 To appear in Data Ming and Knowledge Discovery ...
Guansong Pang, Longbing Cao, Ling Chen
openaire +2 more sources
A Multiscale Clustering Approach for Non‐IID Nominal Data [PDF]
Computational Intelligence and Neuroscience, 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 ...
Runzi Chen, Shuliang Zhao, Zhenzhen Tian
openaire +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 +2 more
openaire +5 more sources
Adaptive Federated Learning With Non-IID Data
The Computer Journal, 2022 Abstract With the widespread use of Internet of things(IoT) devices, it generates an enormous volume of data, and it is a challenge to mine the IoT data value while ensuring security and privacy. Federated learning is a decentralized approach for training data located on edge devices, such as mobile phones and IoT devices, while keeping ...
Yan Zeng +7 more
openaire +1 more source