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cs.DC2026
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…
cs.AI2026
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…