A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
[article]
2022
arXiv
pre-print
Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are usually annotated by clients of varying expertise or criteria and thus contain different amounts of noises. Local training on noisy labels can easily result in overfitting to noisy labels, which is devastating to the global model through aggregation. Although recent robust FL methods take malicious clients into
arXiv:2205.10110v1
fatcat:fcjeh3p6jnfjbinkpesqpqbgnu