Results 11 to 20 of about 14,306 (259)
A Multiscale Clustering Approach for Non-IID Nominal Data. [PDF]
Comput Intell Neurosci, 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 ...
Chen R, Zhao S, Tian Z.
europepmc +4 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 +2 more
openaire +2 more sources
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
openaire +2 more sources
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
doaj +1 more source
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
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
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source
Agronomy, 2023 Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress.
Meenakshi Aggarwal +6 more
doaj +1 more source
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
openaire +2 more sources