The mass adoption of Internet of Things (IoT) devices, and smartphones has given rise to the era of big data and opened up an opportunity to derive data-driven insights. This data deluge drives the need for privacy-aware data computations. In this paper, we highlight the use of an emerging learning paradigm known as federated learning (FL) for vision-aided applications, since it is a privacy preservation mechanism by design. Furthermore, we outline the opportunities, challenges, and future research direction for the FL enabled vision applications.
CITATION STYLE
Khan, A. R., Zoha, A., Mohjazi, L., Sajid, H., Abbasi, Q., & Imran, M. A. (2022). When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 420 LNICST, pp. 308–319). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-95593-9_23
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