When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges

Ahsan Raza Khan; Ahmed Zoha; Lina Mohjazi; Hasan Sajid; Qammar Abbasi; Muhammad Ali Imran

doi:10.1007/978-3-030-95593-9_23

When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges

Ahsan Raza Khan
, Ahmed Zoha
, Lina Mohjazi
, Hasan Sajid
, Qammar Abbasi
, Muhammad Ali Imran

University of Glasgow
National University of Sciences and Technology Pakistan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings
Editors	Masood Ur Rehman, Ahmed Zoha
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	308-319
Number of pages	12
ISBN (Print)	9783030955922
DOIs	https://doi.org/10.1007/978-3-030-95593-9_23
State	Published - 2022
Externally published	Yes
Event	16th EAI International Conference on Body Area Networks, BODYNETS 2021 - Virtual, Online Duration: 25 Dec 2021 → 26 Dec 2021

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	420 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	16th EAI International Conference on Body Area Networks, BODYNETS 2021
City	Virtual, Online
Period	25/12/21 → 26/12/21

Keywords

Collaborative AI
Decentralized data
Edge computing
Federated Learning
Internet-of-Things
Vision analytics

Access to Document

10.1007/978-3-030-95593-9_23

Cite this

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 M. Ur Rehman, & A. Zoha (Eds.), Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings (pp. 308-319). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 420 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-95593-9_23

Khan, Ahsan Raza ; Zoha, Ahmed ; Mohjazi, Lina et al. / When Federated Learning Meets Vision : An Outlook on Opportunities and Challenges. Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. editor / Masood Ur Rehman ; Ahmed Zoha. Springer Science and Business Media Deutschland GmbH, 2022. pp. 308-319 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{65ddfe73816c4ec5adb8f060e89f7206,

title = "When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges",

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.",

keywords = "Collaborative AI, Decentralized data, Edge computing, Federated Learning, Internet-of-Things, Vision analytics",

author = "Khan, \{Ahsan Raza\} and Ahmed Zoha and Lina Mohjazi and Hasan Sajid and Qammar Abbasi and Imran, \{Muhammad Ali\}",

note = "Publisher Copyright: {\textcopyright} 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 16th EAI International Conference on Body Area Networks, BODYNETS 2021 ; Conference date: 25-12-2021 Through 26-12-2021",

year = "2022",

doi = "10.1007/978-3-030-95593-9\_23",

language = "English",

isbn = "9783030955922",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "308--319",

editor = "\{Ur Rehman\}, Masood and Ahmed Zoha",

booktitle = "Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings",

address = "Germany",

}

Khan, AR, Zoha, A, Mohjazi, L, Sajid, H, Abbasi, Q & Imran, MA 2022, When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges. in M Ur Rehman & A Zoha (eds), Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 420 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 308-319, 16th EAI International Conference on Body Area Networks, BODYNETS 2021, Virtual, Online, 25/12/21. https://doi.org/10.1007/978-3-030-95593-9_23

When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges. / Khan, Ahsan Raza; Zoha, Ahmed; Mohjazi, Lina et al.
Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. ed. / Masood Ur Rehman; Ahmed Zoha. Springer Science and Business Media Deutschland GmbH, 2022. p. 308-319 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 420 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - When Federated Learning Meets Vision

T2 - 16th EAI International Conference on Body Area Networks, BODYNETS 2021

AU - Khan, Ahsan Raza

AU - Zoha, Ahmed

AU - Mohjazi, Lina

AU - Sajid, Hasan

AU - Abbasi, Qammar

AU - Imran, Muhammad Ali

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Collaborative AI

KW - Decentralized data

KW - Edge computing

KW - Federated Learning

KW - Internet-of-Things

KW - Vision analytics

UR - https://www.scopus.com/pages/publications/85125281956

U2 - 10.1007/978-3-030-95593-9_23

DO - 10.1007/978-3-030-95593-9_23

M3 - Conference contribution

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SN - 9783030955922

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 308

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BT - Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings

A2 - Ur Rehman, Masood

A2 - Zoha, Ahmed

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 25 December 2021 through 26 December 2021

ER -

Khan AR, Zoha A, Mohjazi L, Sajid H, Abbasi Q, Imran MA. When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges. In Ur Rehman M, Zoha A, editors, Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 308-319. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-95593-9_23

When Federated Learning Meets Vision: An Outlook on Opportunities and Challenges

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Keywords

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