Results 21 to 30 of about 31,140,578 (184)
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
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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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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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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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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
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, 2023 Personalized federated learning simulation platform with non-IID and unbalanced ...
Tsing +3 more
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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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Data augmentation scheme for federated learning with non-IID data
Tongxin xuebao, 2023 To solve the problem that the model accuracy remains low when the data are not independent and identically distributed (non-IID) across different clients in federated learning, a privacy-preserving data augmentation scheme was proposed.Firstly, a data ...
Lingtao TANG, Di WANG, Shengyun LIU
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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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On the Convergence of FedAvg on Non-IID Data
CoRR, 2019 2020 International Conference on Learning ...
Xiang Li 0050 +4 more
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