4 papers
MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
Chamani Shiranthika, Hadi Hadizadeh, Parvaneh Saeedi
Federated Learning enables decentralized training by aggregating model updates across clients without sharing raw data, while Split Federated Learning further partitions the model…
When To Adapt? Adapting the Model or Data in Federated Medical Imaging
Chamani Shiranthika, Parvaneh Saeedi
Federated learning enables collaborative model training across medical institutions without sharing raw data, but its performance is often limited by domain heterogeneity across cl…
MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation
Chamani Shiranthika, Zahra Hafezi Kafshgari, Hadi Hadizadeh +1
Machine Learning (ML) and Deep Learning (DL) have shown significant promise in healthcare, particularly in medical image segmentation, which is crucial for accurate disease diagnos…
SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning
Chamani Shiranthika, Hadi Hadizadeh, Parvaneh Saeedi +1
Federated Learning (FL) enables multiple clients to train a collaborative model without sharing their local data. Split Learning (SL) allows a model to be trained in a split manner…