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• Wissem Soussi, Gürkan Gür, Burkhard Stiller: MERLINS: A Machine Learning-Driven Moving Target Defense (MTD) Framework for Future Networks; 39th IEEE/IFIP Network Operations and Management Symposium 2026, "NOMS 2026, Dissertation Digest Session", Rome, Italy, May 2026, pp 1–7.
• Pascal Kiechl, Katharina Müller, Burkhard Stiller, Rodrigues, Bruno: Distributed Pulse-Wave Simulator for DDoS Dataset Generation; 39th IEEE/IFIP Network Operations and Management Symposium 2026, "NOMS 2026, Experience Session", Rome, Italy, May 2026, pp 1–6.
• Chao Feng, Zihan Liu, Siddhant Gupta, Jan von der Assen: Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive HIL Testing; The 64th Annual Meeting of the Association for Computational Linguistics, San Diego, California, United States, April 2026, pp 1–9.
• Jonas Länzlinger, Katharina O.E. Müller, Burkhard Stiller, Bruno Rodrigues: Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators; IEEE International Conference on Pervasive Computing and Communications - Workshops and Affiliated Events, "PerCom 2026 WIP", Pisa, Italy, March 2026, pp 757–761.
• Enrique Tomás Martínez Beltrán, Eduard Gash, Gérôme Bovet, Alberto Huertas Celdrán, Burkhard Stiller: Asynchronous Cache-based Aggregation with Fairness and Filtering for Decentralized Federated Learning; Elsevier, Computer Networks, Vol. 112225, No. 3, March 2026, ISSN 1389-1286, pp 1–22. doi:http://doi.org/10.1016/j.comnet.2026.112225 URL: https://www.sciencedirect.com/science/article/pii/S1389128626002379?via%3Dihub.
• Gökcan Cantali, Wissem Soussi, Gürkan Gür, Burkhard Stiller: Sustainable Federated Learning in 6G Networks: The Case for Cybersecurity; IEEE, IEEE Communications Magazine, Vol. 64, No. 4, March 2026, pp 1–7.
• Yujie Tao: Create a Swiss Landscape Aesthetics Dataset for Decentralized Federated Learning; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, March 2026, URL: https://files.ifi.uzh.ch/CSG/staff/feng/external/theses/MasterThesis_yujieTao.pdf.
• Chao Feng, Alberto Huertas Celdran, Jing Han, Heqing Ren, Xi Cheng, Zien Zeng, Lucas Krauter, Gerome Bovet, Burkhard Stiller: A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models; Nature, Scientific Data, Vol. 1, No. 1, March 2026, pp 1–17. doi:http://doi.org/10.1038/s41597-026-07155-w URL: https://www.nature.com/articles/s41597-026-07155-w.
• Nasim Nezhadsistani, Mohsen Tajgardan, Mahdi Rabbani, Weijie Niu, Burkhard Stiller: Design and Evaluation of a Robust and Explainable Intrusion Detection Framework for 5G/B5G Networks; Elsevier, Computer Networks, Vol. 278, No. 112100, February 2026, ISSN 1389-1286, pp 1–12. doi:http://doi.org/10.1016/j.comnet.2026.112100 URL: https://www.sciencedirect.com/science/article/pii/S138912862600112X.
• Niklas Schmidt, Elias Müller: Design and Prototypical Implementation of Decentralised Federated Object Detection; Universität Zürich, Communication Systems Group, Department of Informatics, Zürich, Switzerland, February 2026, URL: https://files.ifi.uzh.ch/CSG/staff/feng/external/theses/Master_Project_Niklas_Schmidt_Elias_M%C3%BCller.pdf.