Results 21 to 30 of about 14,306 (259)
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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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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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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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
doaj +1 more source
Federated Graph Classification over Non-IID Graphs
CoRR, 2021 Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can also be regarded as a special type of data samples, which can be collected and stored in separate local systems.
Han Xie +3 more
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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
doaj +1 more source
Ensemble Federated Adversarial Training with Non-IID data
CoRR, 2021 Despite federated learning endows distributed clients with a cooperative training mode under the premise of protecting data privacy and security, the clients are still vulnerable when encountering adversarial samples due to the lack of robustness.
Shuang Luo +3 more
openaire +2 more sources
A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases
IEEE AccessThe rapid advancement in technology has simplified human life and provides convenience. However, this convenience has led to many lifestyle diseases like diabetes and obesity.
Seema Gulati +4 more
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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.
Fangfang Li, Guandong Xu, Longbing Cao
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