Jan 29, 2020 · It maintains a small set of benchmark samples on the FL server and quantifies the credibility of the client local data without directly ...
Dealing with Label Quality Disparity in Federated Learning - SpringerLink
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Nov 26, 2020 · It is designed to identify participants with noisy labels and aggregate their model parameters into the FL model in an opportunistic manner. FOC ...
Federated Learning (FL) is highly useful for the applications which suffer silo effect and privacy preserving, such as healthcare, finance, education, etc.
FOCUS has been experimentally evaluated on both synthetic data and real-world data. The results show that it effectively identifies clients with noisy labels ...
Feb 1, 2023 · We formalize the study of federated learning from heterogeneous label noise by firstly identifying two promising label noise generation models.
Jun 21, 2022 · Since multiple participants may bring low-quality labeled datasets and the corresponding negative effects on model training, the label noise ...
Jan 29, 2020 · Label quality disparity is an important challenge facing to- day's federated learning field. So far, it remains open. Noisy labels in FL clients ...
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Jan 29, 2020 · The results show that FOCUS effectively identifies clients with noisy labels and reduces their impact on the model performance, ...
Federated Learning (FL) heavily depends on label quality for its performance. However, the label distribution among individual clients is always both noisy and ...
Feb 22, 2024 · We propose FedLN , a framework to deal with label noise across different FL training stages, namely FL initialization, on-device model training, ...