FedDSS: A data-similarity approach for client selection in horizontal federated learning
We demonstrated that FedDSS contributes to improved learning in FL by achieving faster convergence, reaching the desired TPR with fewer communication rounds, and potentially enhancing sepsis prediction (TPR) over FedAvg.
Authors
- Shu‐Ling Chong (ORCID: https://orcid.org/0000-0003-4647-0019)
- Jan Hau Lee (ORCID: https://orcid.org/0000-0002-8430-4217)
- Kim Leng Poh (ORCID: https://orcid.org/0000-0003-2974-8090)
- Tuong Minh Nguyen (ORCID: https://orcid.org/0000-0002-8809-4800)
Institutions
- National University of Singapore (SG)
- KK Women's and Children's Hospital (SG)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1016/j.ijmedinf.2024.105650
- Citations
- 4
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 1.39