Matches in SemOpenAlex for { <https://semopenalex.org/work/W4284975292> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W4284975292 abstract "Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patients. Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and devices, lead to distinctive data distributions. Data heterogeneity poses a challenge for FL and the personalization of the local models. In this work, we investigate an adaptive hierarchical clustering method for FL to produce intermediate semi-global models, so clients with similar data distribution have the chance of forming a more specialized model. Our method forms several clusters consisting of clients with the most similar data distributions; then, each cluster continues to train separately. Inside the cluster, we use meta-learning to improve the personalization of the participants' models. We compare the clustering approach with classical FedAvg and centralized training by evaluating our proposed methods on the HAM10k dataset for skin lesion classification with extreme heterogeneous data distribution. Our experiments demonstrate significant performance gain in heterogeneous distribution compared to standard FL methods in classification accuracy. Moreover, we show that the models converge faster if applied in clusters and outperform centralized training while using only a small subset of data." @default.
- W4284975292 created "2022-07-10" @default.
- W4284975292 creator A5014777005 @default.
- W4284975292 creator A5018341171 @default.
- W4284975292 creator A5033627901 @default.
- W4284975292 creator A5046896448 @default.
- W4284975292 creator A5058198220 @default.
- W4284975292 creator A5066823034 @default.
- W4284975292 date "2022-07-07" @default.
- W4284975292 modified "2023-10-01" @default.
- W4284975292 title "Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data" @default.
- W4284975292 doi "https://doi.org/10.48550/arxiv.2207.03448" @default.
- W4284975292 hasPublicationYear "2022" @default.
- W4284975292 type Work @default.
- W4284975292 citedByCount "0" @default.
- W4284975292 crossrefType "posted-content" @default.
- W4284975292 hasAuthorship W4284975292A5014777005 @default.
- W4284975292 hasAuthorship W4284975292A5018341171 @default.
- W4284975292 hasAuthorship W4284975292A5033627901 @default.
- W4284975292 hasAuthorship W4284975292A5046896448 @default.
- W4284975292 hasAuthorship W4284975292A5058198220 @default.
- W4284975292 hasAuthorship W4284975292A5066823034 @default.
- W4284975292 hasBestOaLocation W42849752921 @default.
- W4284975292 hasConcept C110121322 @default.
- W4284975292 hasConcept C119857082 @default.
- W4284975292 hasConcept C124101348 @default.
- W4284975292 hasConcept C134306372 @default.
- W4284975292 hasConcept C136764020 @default.
- W4284975292 hasConcept C154945302 @default.
- W4284975292 hasConcept C164866538 @default.
- W4284975292 hasConcept C183003079 @default.
- W4284975292 hasConcept C199360897 @default.
- W4284975292 hasConcept C2992525071 @default.
- W4284975292 hasConcept C33923547 @default.
- W4284975292 hasConcept C41008148 @default.
- W4284975292 hasConcept C73555534 @default.
- W4284975292 hasConcept C92835128 @default.
- W4284975292 hasConceptScore W4284975292C110121322 @default.
- W4284975292 hasConceptScore W4284975292C119857082 @default.
- W4284975292 hasConceptScore W4284975292C124101348 @default.
- W4284975292 hasConceptScore W4284975292C134306372 @default.
- W4284975292 hasConceptScore W4284975292C136764020 @default.
- W4284975292 hasConceptScore W4284975292C154945302 @default.
- W4284975292 hasConceptScore W4284975292C164866538 @default.
- W4284975292 hasConceptScore W4284975292C183003079 @default.
- W4284975292 hasConceptScore W4284975292C199360897 @default.
- W4284975292 hasConceptScore W4284975292C2992525071 @default.
- W4284975292 hasConceptScore W4284975292C33923547 @default.
- W4284975292 hasConceptScore W4284975292C41008148 @default.
- W4284975292 hasConceptScore W4284975292C73555534 @default.
- W4284975292 hasConceptScore W4284975292C92835128 @default.
- W4284975292 hasLocation W42849752921 @default.
- W4284975292 hasOpenAccess W4284975292 @default.
- W4284975292 hasPrimaryLocation W42849752921 @default.
- W4284975292 hasRelatedWork W194352584 @default.
- W4284975292 hasRelatedWork W2032422752 @default.
- W4284975292 hasRelatedWork W2380798983 @default.
- W4284975292 hasRelatedWork W2394938095 @default.
- W4284975292 hasRelatedWork W2589583093 @default.
- W4284975292 hasRelatedWork W2592952084 @default.
- W4284975292 hasRelatedWork W2888523397 @default.
- W4284975292 hasRelatedWork W3097684051 @default.
- W4284975292 hasRelatedWork W3099482891 @default.
- W4284975292 hasRelatedWork W2104915767 @default.
- W4284975292 isParatext "false" @default.
- W4284975292 isRetracted "false" @default.
- W4284975292 workType "article" @default.