Vojtech Halenka

• Halenka, Vojtech; Amini, Mohammadreza; Andersen, Per-Arne; Granmo, Ole-Christoffer; Jiao, Lei & Kantarci, Burak (2026). Analyzing Backdoor Vulnerabilities in Interpretable Machine Learning for Wireless Jamming Detection Using Tsetlin Machines. IEEE Transactions on Network Science and Engineering (IEEE TNSE). 13, p. 11869–11886. doi: 10.1109/TNSE.2026.3727545. Full text in Research Archive Show summary

  Interpretable machine learning is increasingly sought in next-generation cellular networks, where trust, accountability, and robustness are essential and jamming remains a persistent threat. Federated learning enables collaborative jamming detection without sharing raw in-phase and quadrature data, while Tsetlin Machines provide a logic-based option for transparent, resource-constrained deployments. However, the security implications of interpretability in federated wireless environments remain poorly understood. We introduce a backdoor threat model for interpretable federated jamming detectors, showing how Tsetlin Machine clause structure can be exploited by a compromised client to induce targeted detection failures. In a federated framework with weighted Coalesced Tsetlin Machines and Krum aggregation, we analyze clause-based vulnerability to spectrogram-domain trigger perturbations and formulate a multi-objective problem balancing trigger effectiveness, model deviation, and aggregation selection. We further show that clause-informed trigger initialization lowers adversarial optimization effort, revealing a tension between transparency and robustness. Evaluation on 60,000 spectrograms from a 5G NR synchronization-signal testbed shows that optimized triggers can exceed 70% trial-level triggered false-negative rates in optimizer benchmarks, reaching approximately 90% in best-case configurations; supplemental multi-round federated evaluation shows that trial-level and global triggered false-negative rate (TFR) can diverge substantially (gaps from -31.4 to +3.0 percentage points across evaluated partitions), with no sustained global TFR≥70% after the last malicious selection round. These results highlight the need for defense-aware design of explainable models in next-generation networks.

• Halenka, Vojtech; Kadhim, Ahmed Khalid; Paul, Clarke; Bhattarai, Bimal; Saha, Rupsa & Granmo, Ole-Christoffer [Show all 8 contributors for this article] (2024). Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines. In NN, NN (Eds.), 2024 International Symposium on the Tsetlin Machine (ISTM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331504984. doi: 10.1109/ISTM62799.2024.10931546. Full text in Research Archive Show summary

  Tsetlin machines (TMs) have been successful in several application domains, operating with high efficiency on Boolean representations of the input data. However, Booleanizing complex data structures such as sequences, graphs, images, signal spectra, chemical compounds, and natural language is non-trivial. In this paper, we propose a hypervector (HV) based method for expressing arbitrarily large sets of concepts associated with any input data. Using a hyperdimensional space to build vectors drastically expands the capacity and flexibility of the TM. We demonstrate how images, chemical compounds, and natural language text are encoded according to the proposed method, and how the resulting HV -powered TM can achieve significantly higher accuracy and faster learning on well-known benchmarks. Our results thus open up a new research direction for TMs, namely how to expand and exploit the benefits of operating in hyperspace, including new booleanization strategies, optimization of TM inference and learning, as well as new TM applications.

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