4 papers
DynSL: Resource-Adaptive Model Training across Heterogeneous Edge Devices
Boris Radovič, Istemi Ekin Akkus, Marco Canini +4
Training models over data distributed across edge devices presents a trade-off: centralizing data simplifies training but raises privacy and regulatory concerns, while full local t…
Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs
Boris Radovič, Marco Canini, Veljko Pejović
The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the user. Agents powered by models…
Where is the Testbed for my Federated Learning Research?
Janez Božič, Amândio R. Faustino, Boris Radovič +2
Progressing beyond centralized AI is of paramount importance, yet, distributed AI solutions, in particular various federated learning (FL) algorithms, are often not comprehensively…
REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settings
Boris Radovič, Veljko Pejović
Clustering clients into groups that exhibit relatively homogeneous data distributions represents one of the major means of improving the performance of federated learning (FL) in n…