Federated Learning for Heterogeneous and Inconsistent Data

by Simon Höckerbo | Dec 9, 2025

PI: Anders Eklund, Linköping University co-PI: Mikael Nilsson, Lund University Federated learning (FL) promises to revolutionize AI by enabling collaborative training without sharing sensitive data (see figure). However, current FL methods fail catastrophically with...

Sparse Neural Graphics Using Cross Vendor GPU Optimization

by Simon Höckerbo | Dec 9, 2025

PI: Michael Doggett, Lund University co-PI: Jonas Unger, Linköping University Real-time rendering technology underlies all interactive 3D visual experiences on all displays across all modern computing devices and output modalities. Numerous industrially and societally...

Coding for Secure Federated Learning

by Simon Höckerbo | Dec 9, 2025

PI: Zheng Chen, Linköping University co-PI: Michael Lentmaier, Lund University Federated learning (FL) is a distributed learning paradigm where multiple agents collaborate to solve a common learning task by conducting local training and exchanging model updates with a...

MOUSE: Multimodal Multitask Learning for Reliable Robot Situational Awareness in Adverse Environmental Conditions

by Simon Höckerbo | Dec 9, 2025

PI: Eren Erdal Aksoy, Lund University co-PI: Gustaf Hendeby, Linköping University Autonomous robots require robust perception and navigation in adverse environments where sensors like LiDAR, RADAR, and RGB-D cameras often fail or degrade. We propose MOUSE, a novel...

Learning Robot Control from Expert Demonstrations and Self-Experience

by Simon Höckerbo | Dec 9, 2025

PI: Farnaz Adib Yaghmaie, Linköping University co-PI: Yiannis Karayiannidis, Lund University Mobile manipulation, i.e., manipulation tasks performed by robots that can move through and interact with large, unconstrained environments, requires learning methods that go...