Results 21 to 30 of about 1,704,441 (287)
Evaluation of Remotely Sensed Inundation Data Sets to Estimate Flood‐Associated Emergency Department Visits After Hurricane Harvey [PDF]
GeoHealthFloods can increase the risk of adverse health outcomes through multiple pathways, including contamination of food and water. Remotely sensed (RS) inundation extents can help identify regions with expected heightened flood‐related health risks, but ...
Balaji Ramesh +6 more
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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
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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
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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
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Peer-to-Peer Learning+Consensus with Non-IID Data
2023 57th Asilomar Conference on Signals, Systems, and Computers, 2023 Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a central server. Peer-to-Peer Learning (P2PL) and other algorithms based on Distributed Local-Update Stochastic/mini-batch Gradient Descent (local DSGD) rely on interleaving epochs
Srinivasa Pranav, José M. F. Moura
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Privacy-Enhanced Federated Learning for Non-IID Data
Mathematics, 2023 Federated learning (FL) allows the collaborative training of a collective model by a vast number of decentralized clients while ensuring that these clients’ data remain private and are not shared. In practical situations, the training data utilized in FL
Qingjie Tan, Shuhui Wu, Yuanhong Tao
doaj +1 more source
, 2023 <p>First version of code for benchmarking normalization layers in federated learning for image classification tasks on non-iid data</p ...
Bruno Casella
core +1 more source
A Graph Neural Network Based Decentralized Learning Scheme
Sensors, 2022 As an emerging paradigm considering data privacy and transmission efficiency, decentralized learning aims to acquire a global model using the training data distributed over many user devices.
Huiguo Gao +3 more
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Federated Learning with Non-IID Data
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
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On the Convergence of FedAvg on Non-IID Data
CoRR, 2019 2020 International Conference on Learning ...
Xiang Li 0050 +4 more
openaire +3 more sources