Federated Learning with Trust Evaluation for Industrial Applications
Author:
Affiliation:
1. Rochester Institute of Technology,Rochester,NY,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10194967/10194965/10195142.pdf?arnumber=10195142
Reference8 articles.
1. Effect of Synthetic Minority Oversampling Technique (SMOTE), Feature Representation, and Classification Algorithm on Imbalanced Sentiment Analysis
2. Concepts for federated learning, client classification and training data similarity measurement;samek;US Patent App,2022
3. Improving Knowledge Based Detection of Soft Attacks Against Autonomous Vehicles with Reputation, Trust and Data Quality Service Models
4. Are ML Image Classifiers Robust to Medical Image Quality Degradation?
5. Communication-efficient learning of deep networks from decentralized data;mcmahan;Artificial Intelligence and Statistics,2017
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1. Are Industrial ML Image Classifiers Robust to Data Affected by Network QoS Degradation?;2023 IEEE Western New York Image and Signal Processing Workshop (WNYISPW);2023-11-03
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