A comprehensive review on Federated Learning for Data-Sensitive Application: Open issues & challenges.

Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to analyze data. In both, the data is stored centrally. The data involved may be sensitive and leakage may incur consequences. Applications dealing with intimate data, with critical results, cannot afford this risk and are termed Data-Sensitive Applications (DSA). Some examples are healthcare, finance, etc. The data required for DSA cannot be stored centrally due to large amounts, or isolated data islands. The ML and DL techniques following a data-centralized approach have difficulties in handling the scattered data frequently associated with DSA. Federated Learning (FL) acknowledges the scattered data and provides a more secure and efficient way to analyze such data. This motivates previously reluctant entities like banks to collaborate for variety and quantity of data. Most DSA transitioned to FL, but the migration is not without concerns. These include communication costs, heterogeneity, and malicious attacks. In this paper, we deeply analyze the role of FL in DSA and provide a taxonomy for the studies and implementations of FL. Then we provide an insight into DSA covering works in healthcare and finance. A glance is provided at attempts in non-DSA with possible DSA applications. Finally, we discuss FL's open issues and challenges with their possible solutions. [ABSTRACT FROM AUTHOR]