Results 31 to 40 of about 9,950,038 (297)
Coupled Matrix Factorization Within Non-IID Context [PDF]
, 2015 Recommender systems research has experienced different stages such as from user preference understanding to content analysis. Typical recommendation algorithms were built on the following bases: (1) assuming users and items are IID, namely independent and identically distributed, and (2) focusing on specific aspects such as user preferences or contents.
Li, Fangfang +2 more
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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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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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IEEE Access, 2023 Data scientists in the Natural Language Processing (NLP) field confront the challenge of reconciling the necessity for data-centric analyses with the imperative to safeguard sensitive information, all while managing the substantial costs linked to the ...
Pascal Riedel +5 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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Federated proximal learning with data augmentation for brain tumor classification under heterogeneous data distributions [PDF]
PeerJ Computer ScienceThe increasing use of electronic health records (EHRs) has transformed healthcare management, yet data sharing across institutions remains limited due to privacy concerns.
Swetha Ghanta +5 more
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Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting
Engineering, 2016 While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services.
Longbing Cao
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FedProc: Prototypical contrastive federated learning on non-IID data
Future Generation Computer Systems, 2023 Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally. However, when the local data of all clients are not independent and identically distributed (i.e., non-IID), it is challenging to implement this form of efficient collaborative learning.
Xutong Mu +6 more
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Engineering Proceedings, 2023 Federated learning (FL) is a field in distributed optimization. Therein, the collection of data and training of neural networks (NN) are decentralized, meaning that these tasks are carried out across multiple clients with limited communication and ...
Tobias Sukianto +4 more
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Analysis and Improvement of Entropy Estimators in NIST SP 800-90B for Non-IID Entropy Sources
IACR Transactions on Symmetric Cryptology, 2017 Random number generators (RNGs) are essential for cryptographic applications. In most practical applications, the randomness of RNGs is provided by entropy sources.
Shuangyi Zhu +4 more
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