Federated Learning for Smart and Sustainable Aquaponics: A Decentralized AI Approach for Urban Resilience
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer-Verlag Singapore Pte Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
29th Pacific Asia Conference on Knowledge Discovery and Data Mining-PAKDD-Annual -- JUN 10-13, 2025 -- Sydney, AUSTRALIA
Anahtar Kelimeler
Federated learning, aquaponics, agriculture, transfer learning
Kaynak
Trends and Applications in Knowledge Discovery and Data Mining, Pakdd 2025 Workshops
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
15835
