7 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…
Optimizing Split Points for Error-Resilient SplitFed Learning
Chamani Shiranthika, Parvaneh Saeedi, Ivan V. Bajić
Recent advancements in decentralized learning, such as Federated Learning (FL), Split Learning (SL), and Split Federated Learning (SplitFed), have expanded the potentials of machin…
SplitFed resilience to packet loss: Where to split, that is the question
Chamani Shiranthika, Zahra Hafezi Kafshgari, Parvaneh Saeedi +1
Decentralized machine learning has broadened its scope recently with the invention of Federated Learning (FL), Split Learning (SL), and their hybrids like Split Federated Learning…
Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations
Zahra Hafezi Kafshgari, Chamani Shiranthika, Parvaneh Saeedi +1
SplitFed Learning, a combination of Federated and Split Learning (FL and SL), is one of the most recent developments in the decentralized machine learning domain. In SplitFed learn…