FedMT: Federated Learning with Mixed-type Labels

arXiv:2210.02042v3 Announce Type: replace
Abstract: In federated learning (FL), classifiers (e.g., deep networks) are trained on datasets from multiple centers without exchanging data across them, and thus improves sample efficiency. In the classical setting of FL, the same labeling criterion is usually employed across all centers being involved in training. This constraint greatly limits the applicability of FL. For example, standards used for disease diagnosis are more likely to be different across clinical centers, which mismatches the classical FL setting. …

abstract arxiv classifiers criterion cs.ai cs.dc cs.lg data datasets efficiency federated learning labeling labels mixed multiple networks sample them training type