Results 1 to 10 of about 7,049,980 (289)
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
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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 +2 more
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Effective Non-IID Degree Estimation for Robust Federated Learning in Healthcare Datasets. [PDF]
J Healthc Inform ResChen KY +7 more
europepmc +2 more sources
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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Non-IID Transfer Learning on Graphs
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the
Jun Wu 0019 +2 more
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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
Federated Learning Framework for IID and Non-IID datasets of Medical Images
Emitter: International Journal of Engineering Technology, 2023 Advances have been made in the field of Machine Learning showing that it is an effective tool that can be used for solving real world problems. This success is hugely attributed to the availability of accessible data which is not the case for many fields
Kavitha Srinivasan +3 more
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Frontiers in Neurology, 2022 ObjectiveTo analyze the interictal discharge (IID) patterns on pre-operative scalp electroencephalogram (EEG) and compare the changes in IID patterns after removal of epileptogenic tubers in preschool children with tuberous sclerosis complex (TSC ...
Liu Yuan +10 more
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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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