Applying Federated Learning to Block-Term Tensor Regression for Decentralised Data Analysis of Biomedical Data

Axel Faes, Ashkan Pirmani, Yves Moreau, Liesbet Peeters

IEEE Conference on Federated Learning Technologies and Applications (IEEE FLTA 2025) 2025 Spotlight

Block-Term Tensor Regression (BTTR) has proven to be a powerful tool for modeling complex, high-dimensional data by leveraging multilinear relationships, making it particularly well-suited for applications in healthcare and neuroscience. However, traditional implementations of BTTR rely on centralized datasets, which pose significant privacy risks and hinder collaboration across institutions. To address these challenges, we introduce Federated Block-Term Tensor Regression (FBTTR), an extension of BTTR designed for federated learning scenarios. FBTTR enables decentralized data analysis, allowing institutions to collaboratively build predictive models while preserving data privacy and complying with regulations. FBTTR represents a major step forward in applying tensor regression to federated learning environments. Its performance is evaluated in a case study: heart disease prediction. In this case study, FBTTR is applied to predict heart disease using real-world clinical datasets, outperforming both standard federated learning approaches and centralized BTTR models. In the Fed-Heart-Disease Dataset, an AUC-ROC was obtained of 0.872 $\pm$ 0.02 and an accuracy of 0.772 $\pm$ 0.02 compared to 0.812 $\pm$ 0.003 and 0.753 $\pm$ 0.007 for the centralized model. Empirical evaluations highlight the robustness and scalability of FBTTR, achieving predictive performance comparable to centralized models while significantly reducing privacy concerns. Additionally, the algorithm is computationally efficient, making it viable for real-world deployment where timely predictions are critical. By offering a scalable and privacy-preserving solution, FBTTR has the potential to advance predictive analytics across multiple domains, including healthcare and brain-computer interface applications. Notably, FBTTR is developed as open-source software, promoting transparency and fostering collaboration within the research community.

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Optimizing Federated Block-Term Tensor Regression: Strategy Comparisons and Applications

Axel Faes, Liesbet Peeters

The 3rd International Conference on Foundation and Large Language Models (FLLM2025) [Symposium on Federated Learning and Intelligent Computing Systems (FLICS 2025)] 2025 Spotlight

Block-Term Tensor Regression (BTTR) is a multilinear modeling framework suited for high-dimensional biomedical data, but its centralized implementation conflicts with privacy and regulatory constraints. To overcome this, we propose Federated Block-Term Tensor Regression (FBTTR), which integrates BTTR into a federated learning setting. We systematically evaluate multiple federated aggregation strategies, including FedAvg, FedYogi, and FedAdam, to assess their effect on model performance and stability. Experiments on the BCI Competition IV dataset demonstrate that the choice of strategy strongly influences predictive accuracy: FedAvg yields the most stable and accurate results across subjects, while adaptive methods such as FedYogi and FedAdam show less consistent performance. These findings highlight the importance of strategy selection in federated learning and establish FBTTR as a practical approach for privacy-preserving analysis of multi-institutional biomedical data.

Decoding Sign Language Finger Movements from high-density ECoG using Graph-Optimized Block Term Tensor Regression

Axel Faes, Mariana P. Branco, Anais Van Hoylandt, Elina Keirse, Tom Theys, Nick F. Ramsey, Marc M. Van Hulle

Journal of Neural Engineering (5.4 IF) 2024

Objective A novel method is introduced to regress over the Sign Language finger movements from human electrocorticography (ECoG) recordings. Approach The proposed Graph-Optimized Block-Term Tensor Regression (Go-BTTR) method consists of two components: a deflation-based regression model that sequentially Tucker-decomposes multiway ECoG data into a series of blocks, and a Causal Graph Process (CGP) that accounts for the complex relationship between finger movements when expressing sign language gestures. Prior to each regression block, CGP is applied to decide which fingers should be kept separate or grouped and should therefore be referred to BTTR or its extended version eBTTR, respectively. Main results Two ECoG datasets were used, one recorded in 5 patients expressing 4 hand gestures of the American Sign Language Alphabet, and another in 2 patients expressing all gestures of the Flemish Sign Language Alphabet. As Go-BTTR combines fingers in a flexible way, it can better account for the nonlinear relationship ECoG exhibits when expressing hand gestures, including unintentional finger co-activations. This is reflected by the superior joint finger trajectory predictions compared to eBTTR, and predictions that are on par with BTTR in single finger scenarios. Subject 5, Dataset 1, during the movement of hand gestures reached 0.82 Pearson correlation for the pinky for Go-BTTR compared to 0.8 and 0.72 for eBTTR and BTTR respectively. Significance Our findings show that Go-BTTR is capable of decoding complex hand gestures taken from the Sign Language Alphabet. Go-BTTR also demonstrates computational efficiency, providing a notable benefit when intracranial electrodes are inserted during a patient's pre-surgical evaluation. This efficiency helps reduce the time required for developing and testing a Brain-Computer Interface (BCI) solution.

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Finger movement and coactivation predicted from intracranial brain activity using extended Block-Term Tensor Regression

Axel Faes, Marc M. Van Hulle

Journal of Neural Engineering (5.4 IF) 2023 Spotlight

Multiway- or tensor-based decoding techniques for Brain-Computer Interfaces (BCI) are believed to better account for the multilinear structure of brain signals than conventional vector- or matrix-based ones. However, despite their outlook on significant performance gains, the used parameter optimization approach is often too computationally demanding, so conventional techniques are still preferred. We propose two novel tensor factorizations which we integrate into our Block-Term Tensor Regression (BTTR) algorithm and further introduce a marginalization procedure that guarantees robust predictions while reducing the risk of overfitting (generalized regression). BTTR accounts for the underlying (hidden) data structure in a fully automatic and computationally-efficient manner, leading to a significant performance gain over conventional vector- or matrix-based techniques in a challenging real-world application. As a challenging real-world application, we apply BTTR to accurately predict single finger movement trajectories from intracranial recordings in human subjects. We compare the obtained performance with that of the state-of-the-art.

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