Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
Gaikwad NS, Heublein L, Raichur NL, Feigl T, Mutschler C, Ott F (2025)
Publication Type: Conference contribution
Publication year: 2025
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: Proceedings of IEEE/IFIP Network Operations and Management Symposium 2025, NOMS 2025
Event location: Honolulu, HI, USA
ISBN: 9798331531638
DOI: 10.1109/NOMS57970.2025.11073735
Abstract
Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. Each device trains the model on its local server and shares only the model updates (i.e., gradient weights) during the aggregation step. A significant challenge in FL is managing the feature distribution of novel and unbalanced data across devices. In this paper, we propose an FL approach using few-shot learning and aggregation of the model weights on a global server. We introduce a dynamic early stopping method to balance out-of-distribution classes based on representation learning, specifically utilizing the maximum mean discrepancy of feature embeddings between local and global models. An exemplary application of FL is to orchestrate machine learning models along highways for interference classification based on snapshots from global navigation satellite system (GNSS) receivers. Extensive experiments on four GNSS datasets from two real-world highways and controlled environments demonstrate that our FL method surpasses state-of-the-art techniques in adapting to both novel interference classes and multipath scenarios. https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/federated_learning.
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APA:
Gaikwad, N.S., Heublein, L., Raichur, N.L., Feigl, T., Mutschler, C., & Ott, F. (2025). Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification. In Doug Zuckerman, Mehmet Ulema, Noura Limam, Young-Tak Kim, Lisandro Zambenedetti Granville, Vinicius Fulber-Garcia (Eds.), Proceedings of IEEE/IFIP Network Operations and Management Symposium 2025, NOMS 2025. Honolulu, HI, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Gaikwad, Nishant S., et al. "Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification." Proceedings of the 38th IEEE/IFIP Network Operations and Management Symposium, NOMS 2025, Honolulu, HI, USA Ed. Doug Zuckerman, Mehmet Ulema, Noura Limam, Young-Tak Kim, Lisandro Zambenedetti Granville, Vinicius Fulber-Garcia, Institute of Electrical and Electronics Engineers Inc., 2025.
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