  Machine reading comprehension (MRC) of text data is one important task in
Natural Language Understanding. It is a complex NLP problem with a lot of
ongoing research fueled by the release of the Stanford Question Answering
Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered
to be an effort to teach computers how to "understand" a text, and then to be
able to answer questions about it using deep learning. However, until now
large-scale training on private text data and knowledge sharing has been
missing for this NLP task. Hence, we present FedQAS, a privacy-preserving
machine reading system capable of leveraging large-scale private data without
the need to pool those datasets in a central location. The proposed approach
combines transformer models and federated learning technologies. The system is
developed using the FEDn framework and deployed as a proof-of-concept alliance
initiative. FedQAS is flexible, language-agnostic, and allows intuitive
participation and execution of local model training. In addition, we present
the architecture and implementation of the system, as well as provide a
reference evaluation based on the SQUAD dataset, to showcase how it overcomes
data privacy issues and enables knowledge sharing between alliance members in a
Federated learning setting.
