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cs.LG2026
FedRAW: Preserving Rare-Label Influence in Asynchronous Federated Learning
Prashant Bajpai, Divya Saxena, Philippe Lalanda +1
Asynchronous federated learning improves scalability by updating the global model from a server-side buffer of client updates as they arrive, rather than waiting for all selected c…
cs.LG2026
Efficient and Adaptive Human Activity Recognition via LLM Backbones
Aleksandr Bredikhin, Philippe Lalanda, German Vega
Human Activity Recognition (HAR) is a core task in pervasive computing systems, where models must operate under strict computational constraints while remaining robust to heterogen…
cs.LG2026
Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain Shift
Camille Jimenez Cortes, Philippe Lalanda, German Vega
Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap between in vitro cell lines and pat…