Results 41 to 50 of about 7,049,980 (289)

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), Dianbo Liu (8311149), Li Huang (107408), Yifeng Yin (2992161), Hao Deng (409186), Shifa Zhang (8727138)   +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, Jing Ma 0005, Li Xiong 0001, Carl Yang 0001   +3 more
openaire   +3 more sources

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  

Performance Analysis of Federated Learning Algorithms for Multilingual Protest News Detection Using Pre-Trained DistilBERT and BERT

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, Manfred Reichert, Reinhold Von Schwerin, Alexander Hafner, Daniel Schaudt, Gaurav Singh   +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), Dianbo Liu (8311149), Li Huang (107408), Yifeng Yin (2992161), Hao Deng (409186), Shifa Zhang (8727138)   +5 more
core   +1 more source

Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets [PDF]

, 2022
Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulations.
Marcus, Völp, Maria, Fernandes, Jérémie, Decouchant, Lydia, Chen, LUCCHETTI, Federico   +4 more
core  

A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases

IEEE Access
The 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, Kalpna Guleria, Nitin Goyal, Ahmad Ali AlZubi, Angel Kuc Castilla   +4 more
doaj   +1 more source

TsingZ0/PFL-Non-IID: 32 algorithms

, 2023
<p>Reach a milestone with a total of 32 algorithms in this platform.</p ...
Tsing, NewAlexandria, isaacveg, Youpeng Li, Yuwen Yang, rachel618   +5 more
core   +1 more source

A Collaborative Privacy Preserved Federated Learning Framework for Pneumonia Detection using Diverse Chest X-ray Data Silos [PDF]

International Journal of Mathematical, Engineering and Management Sciences
Pneumonia detection from chest X-rays remains one of the most challenging tasks in the traditional centralized framework due to the requirement of data consolidation at the central location raising data privacy and security concerns.
Shagun Sharma, Kalpna Guleria
doaj   +1 more source

Memory attacks in network nonlocality and self-testing [PDF]

Quantum
We study what can or cannot be certified in communication scenarios where the assumption of independence and identical distribution (iid) between experimental rounds fails.
Mirjam Weilenmann, Costantino Budroni, Miguel Navascues   +2 more
doaj   +1 more source