Results 11 to 20 of about 9,950,038 (297)

Federated Learning with Non-IID Data [PDF]

arXiv.org, 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.
Zhao, Yue, Li, Meng, Lai, Liangzhen, Suda, Naveen, Civin, Damon, Chandra, Vikas   +5 more
openaire   +3 more sources

Federated Conditional Variational Auto Encoders for Cyber Threat Intelligence: Tackling Non-IID Data in SDN Environments

IEEE Access
Federated 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, Rosilah Hassan, Faizan Qamar, Kashif Nisar, Mohammed Azmi Al-Betar   +4 more
doaj   +2 more sources

Federated Learning on Non-IID Data Silos: An Experimental Study [PDF]

IEEE International Conference on Data Engineering, 2021
Due to the increasing privacy concerns and data regulations, training data have been increasingly fragmented, forming distributed databases of multiple “data silos” (e.g., within different organizations and countries).
Q. Li, Yiqun Diao, Quan Chen, Bingsheng He   +3 more
semanticscholar   +1 more source

Effective Non-IID Degree Estimation for Robust Federated Learning in Healthcare Datasets. [PDF]

J Healthc Inform Res
Building unbiased and robust machine learning models using datasets from multiple healthcare systems is critical for addressing the needs of diverse patient populations.
Chen KY, Shyu CR, Tsai YY, Baskett WI, Chang CY, Chou CY, Tsai JJP, Shae ZY.   +7 more
europepmc   +2 more sources

Personalized Cross-Silo Federated Learning on Non-IID Data [PDF]

AAAI Conference on Artificial Intelligence, 2020
Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing
Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, J. Pei, Yong Zhang   +6 more
semanticscholar   +1 more source

Federated learning with hierarchical clustering of local updates to improve training on non-IID data [PDF]

IEEE International Joint Conference on Neural Network, 2020
Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a non-iid (not independent and identically distributed) fashion - as is typical ...
Christopher Briggs, Zhong Fan, Péter András   +2 more
semanticscholar   +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, Faiza Qureshi, Stefanie Lindstaedt, Roman Kern   +3 more
doaj   +1 more source

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, Xiumin Wang, Pan Zhou, Weiwei Wu, Xinglin Zhang   +4 more
doaj   +1 more source

Federated Learning Framework for Brain Tumor Detection Using MRI Images in Non-IID Data Distributions. [PDF]

J Imaging Inform Med
Muntaqim MDZ, Smrity TA.
europepmc   +2 more sources

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