English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Federated medical relation extraction enables multiple clients to train adeep network collaboratively without sharing their raw medical data. In orderto handle the heterogeneous label distribution across clients, most of theexisting works only involve enforcing regularization between local and globalmodels during optimization. In this paper, we fully utilize the models of allclients and propose a novel concept of \textit{major classifier vectors}, wherea group of class vectors is obtained in an ensemble rather than the weightedaverage method on the server. The major classifier vectors are then distributedto all clients and the local training of each client is Contrasted with MajorClassifier vectors (FedCMC), so the local model is not prone to overfitting tothe local label distribution. FedCMC requires only a small amount of additionaltransfer of classifier parameters without any leakage of raw data, extractedrepresentations, and label distributions. Our extensive experiments show thatFedCMC outperforms the other state-of-the-art FL algorithms on three medicalrelation extraction datasets.

Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution