Federated Learning for Smart and Sustainable Aquaponics: A Decentralized AI Approach for Urban Resilience

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dc.contributor.authorKasif, Ahmet
dc.contributor.authorCatal, Cagatay
dc.date.accessioned2026-02-08T15:14:55Z
dc.date.available2026-02-08T15:14:55Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description29th Pacific Asia Conference on Knowledge Discovery and Data Mining-PAKDD-Annual -- JUN 10-13, 2025 -- Sydney, AUSTRALIA
dc.description.abstractThe majority of machine learning models rely on centralized methods, which require large data transfers to central repositories. Federated learning, a decentralized machine learning approach, offers a solution by enabling local computations and aggregating local models to build a global model. While federated learning has been applied in some agricultural domains, its use in aquaponics remains unexplored. Aquaponics, a sustainable agricultural method combining fish farming and soil-less plant production, presents unique opportunities for federated learning applications, particularly in urban farming environments. By using edge-based federated learning, we improve scalability, data privacy, and sustainability and also, reduce data transmission needs in smart urban agriculture. This research, a collaboration between research teams from three countries, highlights how federated learning and deep learning can enhance environmental monitoring and sustainability in urban resilience strategies, particularly in smart agriculture. The Flower framework was used to implement federated learning, and ResNet-18 was employed for fish disease detection. This paper introduces novel contributions in federated learning and deep learning techniques for the management of aquaponics systems, highlighting the potential of these technologies to optimize aquaponics systems' efficiency.
dc.description.sponsorshipQatar Research, Development, and Innovation (QRDI) Council [FSC05-0327-240008]; Qatar University
dc.description.sponsorshipThis research was supported by the Qatar Research, Development, and Innovation (QRDI) Council under Grant [FSC05-0327-240008]. This publication was made possible by the support of Qatar University. The findings achieved herein are solely the responsibility of the authors.
dc.identifier.doi10.1007/978-981-96-8197-6_5
dc.identifier.endpage66
dc.identifier.isbn978-981-96-8196-9
dc.identifier.isbn978-981-96-8197-6
dc.identifier.issn2945-9133
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-105009759316
dc.identifier.scopusqualityQ3
dc.identifier.startpage56
dc.identifier.urihttps://doi.org/10.1007/978-981-96-8197-6_5
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5480
dc.identifier.volume15835
dc.identifier.wosWOS:001546494900005
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer-Verlag Singapore Pte Ltd
dc.relation.ispartofTrends and Applications in Knowledge Discovery and Data Mining, Pakdd 2025 Workshops
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectFederated learning
dc.subjectaquaponics
dc.subjectagriculture
dc.subjecttransfer learning
dc.titleFederated Learning for Smart and Sustainable Aquaponics: A Decentralized AI Approach for Urban Resilience
dc.typeConference Object

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