News and Announcements

  • Paper Presentation at IEEE INFOCOM 2026

    May 21, 2026

    Our team member Dr. Youming Tao presented our paper titled Certified Unlearning in Decentralized Federated Learning at the 45th IEEE International Conference on Computer Communications (INFOCOM 2026), which took place in Tokyo, Japan.

  • Jorge Torres Gomez Appointed Educational Services Chair of IEEE TWG MBMC

    May 18, 2026

    Our team member Jorge Torres Gómez has been appointed as Educational Services Chair of the IEEE Technical Working Group Molecular, Biological and Multi-Scale Communications (IEEE TWG MBMC). Main activities on the Educational Services include the preparation of workshops on interdisciplinary research between computer science related disciplines and life sciences, preparation of summer schools and promoting teaching aspects of molecular communications.

  • TKN at the Annual Berlin Firmenlauf

    May 18, 2026

    Some of our team members represented TKN at the annual Berlin Firmenlauf and successfully completed the 5 km run in less than 30 minutes.

  • Paper Presentation at IEEE 6G AI-RAN 2026

    May 18, 2026

    Our team member Osman Tugay Basaran presented our paper titled BRAIN: Bayesian Reasoning via Active Inference for Explainable Mobile Networks at the 1st IEEE Workshop on AI Native Distributed Intelligence for 6G Networks (6G AI-RAN 2026), 45th IEEE International Conference on Computer Communications (INFOCOM 2026), which took place in Tokyo, Japan.

  • New IEEE Communications Surveys & Tutorials article

    May 12, 2026

    Our article Communicating Smartly in Molecular Communication Environments: Neural Networks in the Internet of Bio-Nano Things has been accepted for publication in IEEE Communications Surveys & Tutorials. Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the foundation for innovative healthcare applications. Nanodevices designed to operate within the human body and managed remotely via the Internet are envisioned to detect and respond to diseases promptly. To explore the limits of nanodevice interconnectivity, this survey focuses on data-driven communication strategies for molecular communication (MC) systems interconnecting nanosensors. Due to the complex and dynamic nature of MC environments, accurate physical modeling is often infeasible. Consequently, the MC research community increasingly relies on machine learning (ML) methods, particularly neural network (NN) architectures, to enable robust and adaptive communication at the nanoscale level. This interdisciplinary field spans several aspects, including NNs for communication in IoBNT networks, their nanoscale implementation, explainable approaches, and the generation of training datasets. Within this survey, we provide a comprehensive analysis of current NN architectures for MC, assess their feasibility for nanoscale deployment, review applied explainable artificial intelligence (XAI) techniques, and summarize available datasets along with best practices for their generation. We also include open-source code examples to support reproducible research across key MC scenarios. Finally, we identify emerging challenges, including robust NN architectures, biologically integrated NN modules, and scalable training strategies.
    (link to more information)

  • New Computers and Electrical Engineering article

    May 09, 2026

    Our article Asynchronous 2-layer Full-duplex Cooperative RSMA with Imperfect Channel State Information and Imperfect Successive Interference Cancellation has been accepted for publication in Elsevier Computers and Electrical Engineering. Rate-Splitting Multiple Access (RSMA) has emerged as a strong candidate for 6G wireless access due to its efficient interference management and robustness to imperfect Channel State Information (CSI). However, its performance is often limited by the weakest user, and existing cooperative approaches mainly rely on half-duplex relaying and ideal assumptions. In this paper, a downlink full-duplex multi-user 2-layer cooperative RSMA (C-RSMA) framework is proposed under asynchronous reception, imperfect CSI, and imperfect Successive Interference Cancellation (SIC). The 2-layer structure enhances interference mitigation and fairness, while full-duplex relaying improves spectral efficiency. An alternative optimization technique based on Weighted Minimum Mean Square Error (WMMSE) is used to jointly optimize precoding, rate allocation, and relay power to maximize the minimum user rate under latency constraints. Numerical results show that the proposed scheme enhances fairness and robustness over other schemes.
    (link to more information)

  • New staff member: Bahram Hedayati

    May 03, 2026

    We welcome Bahram Hedayati who joined our group in May 2026.

  • Paper Presentation at MolCom 2026

    April 15, 2026

    Our team member Saswati Pal presented our paper Toward Clinically-Inspired Validation of ML-Driven Source Localization in Molecular Communication at the 10th Workshop on Molecular Communications (MolCom 2026), Istanbul, Turkey. Accurate localization of tumor sources in the human circulatory system is essential for precision oncology. In prior work, we developed a machine learning (ML) framework to localize anomaly sources using temporal biomarker profiles measured at receiver sites. This work-in-progress paper extends the framework by validating the ML model on a clinically-inspired dataset that emulates endocrine signaling in a controlled synthetic environment. Preliminary results show an accuracy of 90%, indicating the potential of ML-driven approaches for tumor source localization in clinically relevant molecular communication settings.

  • Kick-off new BMFTR project NEXT-G

    April 08, 2026

    We are happy to announce our new project "NEXT-G: Explainable and Trustworthy AI/ML for 6G and Beyond", funded by BMFTR, and carried out within the Software Campus program in collaboration with Huawei Munich Research HQ. The project is led by our team member Osman Tugay Basaran as Project Manager, together with a three-researcher team. In this project, we investigate explainable, trustworthy, and robust AI/ML methods for AI-native 6G, with a focus on transparent network intelligence, low-latency explainability, adversarial robustness for connected robotics use cases in smart hospitals and factories.

  • New IEEE Transactions on Networking article

    April 07, 2026

    Our article Byzantine-Resilient Federated Learning under Heterogeneity and Heavy Tails has been accepted for publication in IEEE Transactions on Networking. Byzantine resilience is essential in federated learning (FL) to safeguard model training from malicious or faulty participants. However, existing Byzantine-resilient methods struggle when faced with heavy-tailed gradient noise, a common challenge in heterogeneous environments. In this work, we propose a Byzantine-resilient FL framework specifically designed to handle both heterogeneity and heavy-tailed noise. Our approach builds on robust distributed stochastic heavy-ball optimization, incorporating update normalization and gradient/momentum clipping to mitigate the effects of heavy-tailed noise. We establish the first high-probability convergence guarantees for Byzantine-resilient FL under these conditions, showing that our algorithms achieve optimal Byzantine resilience and align with known lower bounds. Additionally, we introduce an efficient variant of the nearest neighbor mixing technique, leveraging random projections to significantly reduce computational costs in high-dimensional settings. Through rigorous theoretical analysis and extensive empirical evaluations, we demonstrate that our methods outperform existing approaches in robustness against both Byzantine failures and heavy-tailed noise.
    (link to more information)

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Last modified: 2024-04-28