442 citations · 715 across the 42 of their papers we have counts for
13 papers · 1 filter
QoS-Aware Federated Learning for Multimodal In-Cabin Interaction in Smart Vehicles
Baran Can Gül, Mert Nakıp, Nasser Jazdi +1
Modern smart vehicles leverage multimodal sensors, ranging from high-bandwidth vision systems to low-rate physiological monitors, to provide personalized in-cabin services. However…
Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles
Matthias Weiß, Athreya Hosahalli Prakash, Maurice Artelt +3
Connected vehicles are autonomous cyber-physical systems whose behavior must be continuously monitored during operation to detect deviations from normal operation before they propa…
FedMultiEmo: Real-Time Emotion Recognition via Multimodal Federated Learning
Baran Can Gül, Suraksha Nadig, Stefanos Tziampazis +2
In-vehicle emotion recognition underpins adaptive driver-assistance systems and, ultimately, occupant safety. However, practical deployment is hindered by (i) modality fragility -…
SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization
Baran Can Gül, Stefanos Tziampazis, Nasser Jazdi +1
As Federated Learning (FL) expands to larger and more distributed environments, consistency in training is challenged by network-induced delays, clock unsynchronicity, and variabil…
Stuttgart Open Relay Degradation Dataset (SOReDD)
Benjamin Maschler, Angel Iliev, Thi Thu Huong Pham +1
Real-life industrial use cases for machine learning oftentimes involve heterogeneous and dynamic assets, processes and data, resulting in a need to continuously adapt the learning…
Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection
Benjamin Maschler, Tim Knodel, Michael Weyrich
Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropria…