Projects
My research focuses on making federated learning practical — designing architectures, aggregation methods, and training pipelines that work when data is distributed, heterogeneous, and can't be centralized. Below are selected projects; a full publication list follows.
ANFR
TMLR 2025
An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration
FedCLAM Spotlight
MICCAI 2025
Client-Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation
ARIA
IEEE ISBI 2024
On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
FeRaL
MSc Thesis
A Federated Reinforcement Learning Framework and Library, Applied to Sepsis Treatment
Publications
TMLR 2025
An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration
MICCAI 2025
FedCLAM: Client Adaptive Momentum with Foreground Intensity Matching for Federated Medical Image Segmentation