3 papers
cs.LG2025
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework
ErtuÄrul Keçeci, Ertuğrul Keçeci, Müjde Güzelkaya +2
This paper presents FedAlign, a Federated Learning (FL) framework particularly designed for System Identification (SYSID) tasks by aligning state representations. Local workers can…
cs.LG2025
Introducing Fractional Classification Loss for Robust Learning with Noisy Labels
Mert Can Kurucu, Tufan Kumbasar, Ä°brahim Eksin +1
Robust loss functions are crucial for training deep neural networks in the presence of label noise, yet existing approaches require extensive, dataset-specific hyperparameter tunin…
cs.LG2025
Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraft
ErtuÄrul Keçeci, Müjde Güzelkaya, Tufan Kumbasar
This paper addresses the System Identification (SYSID) problem within the framework of federated learning. We introduce a novel algorithm, Incremental Clustering-based federated le…