The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive...

Objective. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Electroencephalography (EEG) brainwave decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has been helpful in neural activity analysis and aiding paralysed patients via brain-computer interfaces. However, training machine learning algorithms on EEG encounters two primary challenges: variability across data sets and privacy concerns using data from individuals and data centres. Our objective is to address these challenges by integrating transfer learning for data variability and federated learning for data privacy into a unified approach.Approach. We introduce the 'Sandwich' as a novel deep privacy-preserving meta-framework combining transfer learning and federated learning. The 'Sandwich' framework comprises three components: federated networks (first layers) that handle data set differences at the input level, a shared network (middle layer) learning common rules and applying transfer learning techniques, and individual classifiers (final layers) for specific brain tasks of each data set. This structure enables the central network (central server) to benefit from multiple data sets, while local branches (local servers) maintain data and label privacy.Main results. We evaluated the 'Sandwich' meta-architecture in various configurations using the BEETL motor imagery challenge, a benchmark for heterogeneous EEG data sets. Compared with baseline models likeShallow ConvNetandEEGInception, our 'Sandwich' implementations showed superior performance. The best-performing model, the Inception SanDwich with deep set alignment (Inception-SD-Deepset), exceeded baseline methods by 9%.Significance. The 'Sandwich' framework demonstrates advancements in federated deep transfer learning for diverse tasks and data sets. It outperforms conventional deep learning methods, showcasing the potential for effective use of larger, heterogeneous data sets with enhanced privacy. In addition, through its diverse implementations with various backbone architectures and transfer learning approaches, the 'Sandwich' framework shows the potential as a model-agnostic meta-framework for decoding time series data like EEG, suggesting a direction towards large-scale brainwave decoding by combining deep transfer learning with privacy-preserving federated learning.