7 papers
SplitFed-CL: A Split Federated Co-Learning Framework for Medical Image Segmentation with Inaccurate Labels
Zahra Hafezi Kafshgari, Hadi Hadizadeh, Parvaneh Saeedi
Split Federated Learning (SplitFed) combines federated and split learning to preserve privacy while reducing client-side computation. However, in medical image segmentation, hetero…
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…
Smart Split-Federated Learning over Noisy Channels for Embryo Image Segmentation
Zahra Hafezi Kafshgari, Ivan V. Bajic, Parvaneh Saeedi
Split-Federated (SplitFed) learning is an extension of federated learning that places minimal requirements on the clients computing infrastructure, since only a small portion of th…
Reinforcement Learning for Unsupervised Video Summarization with Reward Generator Training
Mehryar Abbasi, Hadi Hadizadeh, Parvaneh Saeedi
This paper presents a novel approach for unsupervised video summarization using reinforcement learning (RL), addressing limitations like unstable adversarial training and reliance…
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…