Results 271 to 280 of about 9,950,038 (297)

Non-IIDness Learning in Behavioral and Social Data

The Computer Journal, 2013
Most of the classic theoretical systems and tools in statistics, data mining and machine learning are built on the fundamental assumption of IIDness, which assumes the independence and identical distribution of underlying objects, attributes and/or values.
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Non-IID Learning

IEEE Intelligent Systems, 2022
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Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

arXiv.org
Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively train ML models without sharing their private data.
Daniel Mauricio Jimenez Gutierrez, David Solans, Mikko A. Heikkilä, Andrea Vitaletti, Nicolas Kourtellis, Aris Anagnostopoulos, Ioannis Chatzigiannakis   +6 more
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Non-IID Federated Learning

IEEE Intelligent Systems, 2022
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Efficient Split Learning with Non-iid Data

2022 23rd IEEE International Conference on Mobile Data Management (MDM), 2022
Yuanqin Cai, Tongquan Wei
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FedClust: Optimizing Federated Learning on Non-IID Data Through Weight-Driven Client Clustering

IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum
Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data.
Md Sirajul Islam, Simin Javaherian, Fei Xu, Xu Yuan, Li Chen, Nianfeng Tzeng   +5 more
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Hypernetworks-Based Hierarchical Federated Learning on Hybrid Non-IID Datasets for Digital Twin in Industrial IoT

IEEE Transactions on Network Science and Engineering
Digital twin technology deployed in the industrial Internet of Things (IoT) will be unavailable because of data heterogeneity and data islands. As a machine learning approach for distributed architectures, federated learning generates a global model for ...
Jihao Yang, Wen Jiang, Laisen Nie
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FedSea: Federated Learning via Selective Feature Alignment for Non-IID Multimodal Data

IEEE transactions on multimedia
The growing demands for privacy protection challenge the joint training of one model by leveraging multiple datasets. Federated learning (FL) provides a new way to overcome this challenge and has attracted many research interests, which enables multiple ...
Min Tan, Yinfu Feng, Lingqiang Chu, Jingcheng Shi, Rong Xiao, Haihong Tang, Jun Yu   +6 more
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Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data

Neural Information Processing Systems
Federated learning has become a pivotal distributed learning paradigm, involving collaborative model updates across multiple nodes with private data. However, handling non-i.i.d.
Xiaohong Chen, Canran Xiao, Yongmei Liu
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Federated Dictionary Learning from Non-IID Data

2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2022
Alexandros Gkillas, Dimitris Ampeliotis, Kostas Berberidis   +2 more
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