Results 21 to 30 of about 9,950,038 (297)
FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments. [PDF]
Sensors (Basel)With the rapid advancement of Internet of Things (IoT) technology, intrusion detection systems (IDSs) have become pivotal in ensuring network security. However, the data produced by IoT devices is typically sensitive and tends to display non-independent ...
Peng H, Wu C, Xiao Y.
europepmc +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
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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
Wu, Jun +2 more
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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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NI-DBSCAN: DBSCAN under Non-IID
Journal of Physics: Conference Series, 2020 Abstract DBSCAN (Density Based Spatial Clustering of Application with Noise) is an example of density-based clustering algorithm. Aiming at problem that DBSCAN algorithm assumes that the data are independent and identically distributed and the traditional distance formula is difficult to accurately calculate the similarity degree between
Yikun Lv, He Jiang, Pinchen Pan
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Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [PDF]
Computer Vision and Pattern Recognition, 2022 Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance.
Lin Zhang +4 more
semanticscholar +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
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FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction [PDF]
Computer Vision and Pattern Recognition, 2022 Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity ...
Liang Gao +5 more
semanticscholar +1 more source
Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment
IEEE Access, 2023 Machine Learning (ML) algorithms process input data making it possible to recognize and extract patterns from a large data volume. Likewise, Internet of Things (IoT) devices provide knowledge in a Federated Learning (FL) environment, sharing parameters ...
Fernanda C. Orlandi +4 more
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