Results 41 to 50 of about 10,022,386 (288)
Fast converging Federated Learning with Non-IID Data
2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 2023 With the advancement of device capabilities, Internet of Things (IoT) devices can employ built-in hardware to perform machine learning (ML) tasks, extending their horizons in many promising directions. In traditional ML, data are sent to a server for training. However, this approach raises user privacy concerns.
Sigg Stephan, Naas Si Ahmed
openaire +2 more sources
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
Performance gap between IID and non-IID data.
, 2020 Performance gap between IID and non-IID data.
Zeng Fu (8727135) +5 more
core +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
openaire +4 more sources
Non-IID scenario: 10-fold cross validation results with varying C.
, 2020 Non-IID scenario: 10-fold cross validation results with varying C.
Zeng Fu (8727135) +5 more
core +1 more source
Non-IID Learning for Recommendation, Time Series and Hashing [PDF]
, 2023 University of Technology Sydney. Faculty of Engineering and Information Technology.For several decades, the independent and identically distributed (short for IID) assumption has laid the foundation of data learning, simplifying real-world data's ...
Zhang, Qi
core +1 more source
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
doaj +1 more source
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
Non-IID scenario: 10-fold cross validation results with varying α and β.
, 2020 Non-IID scenario: 10-fold cross validation results with varying α and β.
Zeng Fu (8727135) +5 more
core +1 more source
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
doaj +1 more source