7 citations · 12 across the 7 of their papers we have counts for
4 papers · 1 filter
The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning
Akis Linardos, Sarthak Pati, Ujjwal Baid +25
We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in m…
Proof of Cloud: Data Center Execution Assurance for Confidential VMs
Filip Rezabek, Moe Mahhouk, Andrew Miller +3
Confidential Virtual Machines (CVMs) protect data in use by running workloads within hardware-enforced Trusted Execution Environments (TEEs). However, existing CVM attestation mech…
FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation
Vasilis Siomos, Jonathan Passerat-Palmbach, Giacomo Tarroni
Federated learning is a decentralized training approach that keeps data under stakeholder control while achieving superior performance over isolated training. While inter-instituti…
Narrowing the Gap between TEEs Threat Model and Deployment Strategies
Filip Rezabek, Jonathan Passerat-Palmbach, Moe Mahhouk +2
Confidential Virtual Machines (CVMs) provide isolation guarantees for data in use, but their threat model does not include physical level protection and side-channel attacks. There…