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FedRISE: Rating Induced Sign Election of Gradients for Byzantine Tolerant Federated Aggregation

Autor: Benjamin, Joseph Geo, Asokan, Mothilal, Yaqub, Mohammad, Nandakumar, Karthik

One of the most common defense strategies against model poisoning in federated learning is to employ a robust aggregator mechanism that makes the training more resilient. Many of the existing Byzantine robust aggregators provide theoretical guarantee

Externí odkaz: http://arxiv.org/abs/2411.03861

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Leveraging Self-Supervised Learning for Fetal Cardiac Planes Classification using Ultrasound Scan Videos

Autor: Benjamin, Joseph Geo, Asokan, Mothilal, Alhosani, Amna, Alasmawi, Hussain, Diehl, Werner Gerhard, Bricker, Leanne, Nandakumar, Karthik, Yaqub, Mohammad

Self-supervised learning (SSL) methods are popular since they can address situations with limited annotated data by directly utilising the underlying data distribution. However, the adoption of such methods is not explored enough in ultrasound (US) i

Externí odkaz: http://arxiv.org/abs/2407.21738

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A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation

Autor: Asokan, Mothilal, Benjamin, Joseph Geo, Yaqub, Mohammad, Nandakumar, Karthik

Adapting foundation models for medical image analysis requires finetuning them on a considerable amount of data because of extreme distribution shifts between natural (source) data used for pretraining and medical (target) data. However, collecting t

Externí odkaz: http://arxiv.org/abs/2407.21739

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