Rahman, IftekharIftekharRahmanManikku Badu, Nisal HemadasaNisal HemadasaManikku BaduKaaser, DominikDominikKaaserMurena, Pierre-AlexandrePierre-AlexandreMurenaSchulte, StefanStefanSchulte2026-02-092026-02-092025-103rd International Conference on Federated Learning Technologies and Applications, FLTA 2025https://hdl.handle.net/11420/61457Federated Learning (FL) is a distributed machine learning paradigm where a central server coordinates the training of a global model across multiple decentralized clients. Most FL algorithms assume stationary data-generating processes and neglect concept drift, i.e., the change of data distributions over time. This results in a gradual decline of the model's performance and eventually renders the model obsolete. We investigate the impact of concept drift in FL and propose an active approach to detect and adapt to drift. Our detection method is based on local model loss which allows us to categorize clients based on the type of drift they experience and apply appropriate adaptive measures. Through extensive experiments, we demonstrate the efficiency, accuracy, and precision of our approach: the proposed adaptation approach successfully accelerates convergence for drifted clients, outperforming the baseline algorithm. Even under extreme drift conditions, our global model remains stable. This demonstrates the superior robustness of our approach compared to conventional methods.enMachine LearningFederated LearningConcept DriftModel AdaptationDrift DetectionNon-IID DataTechnology::600: TechnologyComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.31: Machine LearningConference Paper10.1109/flta67013.2025.11336319Conference Paper