1 citations · 2 across the 11 of their papers we have counts for
8 papers · 1 filter
Cross-Architecture Foundation-Model Distillation for Edge Flood Segmentation
Fabian Schmalstieg, Karsten Mueller, Wojciech Samek
Geospatial foundation models can provide strong flood-segmentation performance, but their size limits deployment on memory-constrained edge hardware. We distill a 300-million-param…
A Privacy Study of Sparse Collaborative Inference
Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek
Collaborative inference (CI) splits a model between an edge device and a server, whereby the client computes an intermediate activation, transmits it, and the server completes the…
Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek
One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality…
FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek
Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancin…
INDEQS: Informed Neural controlled Differential EQuationS
Michael Detzel, Gabriel Nobis, Kristiyan Blagov +4
Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatia…
Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs
Nico Harder, Daniel Becking, Karsten Mueller +1
We present Activation- and Influence-Aware Ranks (AIR), an SVD-based LLM compression framework that guides each weight matrix's low-rank approximation with a backward-signal influe…
Knowledge-Free Correlated Agreement for Incentivizing Federated Learning
Leon Witt, Togrul Abbasli, Kentaroh Toyoda +2
We introduce Knowledge-Free Correlated Agreement (KFCA) to reward client contributions in federated learning (FL) without relying on ground truth, a public test set, or distributio…
Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction
Leon Witt, Kentaroh Toyoda, Wojciech Samek +1
The synergy between Federated Learning and blockchain has been considered promising; however, the computationally intensive nature of contribution measurement conflicts with the st…