3 papers
cs.LG2026
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
Christian Internò, Elena Raponi, Markus Olhofer +5
The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by…
cs.NE2025
Cascading CMA-ES Instances for Generating Input-diverse Solution Batches
Maria Laura Santoni, Christoph Dürr, Carola Doerr +2
Rather than obtaining a single good solution for a given optimization problem, users often seek alternative design choices, because the best-found solution may perform poorly with…
cs.LG2025
Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization
Maria Laura Santoni, Elena Raponi, Aneta Neumann +3
In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is hence important to consider more solutions that decision mak…