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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The most challenging, yet practical, setting of semi-supervised federatedlearning (SSFL) is where a few clients have fully labeled data whereas theother clients have fully unlabeled data. This is particularly common inhealthcare settings where collaborating partners (typically hospitals) may haveimages but not annotations. The bottleneck in this setting is the jointtraining of labeled and unlabeled clients as the objective function for eachclient varies based on the availability of labels. This paper investigates analternative way for effective training with labeled and unlabeled clients in afederated setting. We propose a novel learning scheme specifically designed forSSFL which we call Isolated Federated Learning (IsoFed) that circumvents theproblem by avoiding simple averaging of supervised and semi-supervised modelstogether. In particular, our training approach consists of two parts - (a)isolated aggregation of labeled and unlabeled client models, and (b) localself-supervised pretraining of isolated global models in all clients. Weevaluate our model performance on medical image datasets of four differentmodalities publicly available within the biomedical image classificationbenchmark MedMNIST. We further vary the proportion of labeled clients and thedegree of heterogeneity to demonstrate the effectiveness of the proposed methodunder varied experimental settings.

Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data