# FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
## 5 Conclusions  
In this paper, we have proposed FedQAS, a high-quality question answering (FedQAS) approach, to address the data-sharing issue in machine learning. Validation experiments for FedQAS was implemented based on 5 rounds of training with a transformer neural network. The system consists of several components including the private data pipeline, collaborative training and private incremental learning. Experiments on the SQuAD dataset using the transformer architecture demonstrate that our FedQAS significantly outperforms the baseline model performances, protecting data privacy and sharing knowledge within an alliance. The proposed FedQAS allows collaborators (collaborative training participants) to have overall control of their sensitive and private data while collaboratively training question answering models. The integration of federated learning within machine reading comprehension provides a sustainable solution by preserving data privacy and ensuring low-cost training. We conclude that the application of FL to NLP tasks such as question answering can contribute to solving the problem that arises when using machine learning in the context of data protection and privacy. The system actively supports end-users in joining training and improving the performance through incremental learning on a various range of local clients.  
In future work, we aim to extend the FedQAS to cover more datasets in a geographically distributed manner and test the model with other aggregation algorithms (e.g., FedOPT, FedProx, etc.) for federated learning. We also plan to fine-tune the pre-trained models such as BERT large, GPT-2 for MRC and particularly investigate their effectiveness in federated learning settings especially when it comes to large private documents (text).  
Formal analysis, A.A.-M.; investigation, A.A.-M.; methodology, A.A.-M.; project administration, A.H.; writing--review and editing, S.A.A., S.T. and A.H. All authors have read and agreed to the published version of the manuscript. Funding has been provided by the eSSENCE strategic collaboration on eScience ([PERSON], [PERSON], [PERSON], and [PERSON]) and the Swedish Innovation Agency Vimnova grant no. 2019-02819 (awarded to Scaleout Systems AB). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.  
Data Availability Statement: The data used to support the findings of this work can be found on this link [[https://github.com/aitmlouk/FEDn-client-FedQAS-tf/tree/main/data](https://github.com/aitmlouk/FEDn-client-FedQAS-tf/tree/main/data)]([https://github.com/aitmlouk/FEDn-client-FedQAS-tf/tree/main/data](https://github.com/aitmlouk/FEDn-client-FedQAS-tf/tree/main/data)), accessed on 24 January 2022.  
The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at C3 SE partially funded by the Swedish Research Council through grant agreement no. 2018-05973.  
The authors declare no conflict of interest regarding the design of the study; the collection, analyses, or interpretation of data; the writing of the manuscript, or the decision to publish the results.