Research Article
When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges
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@INPROCEEDINGS{10.1007/978-3-030-95593-9_23, author={Ahsan Raza Khan and Ahmed Zoha and Lina Mohjazi and Hasan Sajid and Qammar Abbasi and Muhammad Ali Imran}, title={When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings}, proceedings_a={BODYNETS}, year={2022}, month={2}, keywords={Federated Learning Vision analytics Edge computing Decentralized data Internet-of-Things Collaborative AI}, doi={10.1007/978-3-030-95593-9_23} }- Ahsan Raza Khan
Ahmed Zoha
Lina Mohjazi
Hasan Sajid
Qammar Abbasi
Muhammad Ali Imran
Year: 2022
When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges
BODYNETS
Springer
DOI: 10.1007/978-3-030-95593-9_23
Abstract
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.
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