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

Block-Term Tensor Regression (BTTR) is a multilin-ear 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, 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 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.