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Found 5 papers, 1 papers with code
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
Federated learning (FL) enables multiple data owners (a. k. a.
Fairness-Aware Client Selection for Federated Learning
By not using threshold-based reputation filtering, it provides FL clients with opportunities to redeem their reputations after a perceived poor performance, thereby further enhancing fair client treatment.
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging
Therefore, we propose a reliable multi-view learning method with interpretable combination rules, which can model global representations to improve the accuracy of predictions.
Towards Fairness-Aware Federated Learning
However, most current works focus on the interest of the central controller in FL, and overlook the interests of the FL clients.
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients.
