3 papers
cs.LG2025
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Manuel Röder, Christoph Raab, Frank-Michael Schleif
Federated Learning has emerged as a leading paradigm for decentralized, privacy-preserving learning, particularly relevant in the era of interconnected edge devices equipped with s…
cs.LG2024
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data
Manuel Röder, Frank-Michael Schleif
This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training fr…
cs.LG2024
Sparse Uncertainty-Informed Sampling from Federated Streaming Data
Manuel Röder, Frank-Michael Schleif
We present a numerically robust, computationally efficient approach for non-I.I.D. data stream sampling in federated client systems, where resources are limited and labeled data fo…