Implementing server-side federated learning in an edge-cloud framework for precision aquaculture
This project is about a growing need in the aquaculture industry which is precision aquaculture. Precision aquaculture involves the use of smart technologies such as sensors, cloud computing, and artificial intelligence to monitor and manage fish farming environments. However, small-scale farmers...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/6990/1/fyp_CS_2025_NBJH.pdf http://eprints.utar.edu.my/6990/ |
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| _version_ | 1854094452755267584 |
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| author | Ng, Bryan Jing Hong |
| author_facet | Ng, Bryan Jing Hong |
| author_sort | Ng, Bryan Jing Hong |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This project is about a growing need in the aquaculture industry which is precision
aquaculture. Precision aquaculture involves the use of smart technologies such as
sensors, cloud computing, and artificial intelligence to monitor and manage fish
farming environments. However, small-scale farmers still face major challenges such
as high implementation costs, poor internet connectivity, and concerns about data
privacy. This project focuses on two key problems which are data privacy and system
scalability. Most existing systems rely heavily on cloud connectivity and do not provide
secure or efficient solutions for farms in remote areas with limited internet access. In
this project, research and literature reviews were conducted to explore the current
technologies in smart aquaculture, federated learning, and data privacy. Reviews
include systems using IoT and AI-based models, along with analysis of different
federated learning algorithms such as FedSGD, FedAvg, and FedProx, and privacypreserving methods such as DA, SA, HE and CKKS encryption. After identifying the
gaps in current systems, this project proposes a secure and scalable server-side
federated learning framework in an edge-cloud architecture for precision aquaculture.
The system is designed to enable encrypted model training at the edge using IoT sensors
and Raspberry Pi, while a federated learning server hosted on AWS aggregates updates
without accessing raw data. The final product integrates edge computing, federated
learning, encryption and cloud storage to create a privacy-preserving and cost-effective
solution for monitoring aquaculture environments and supporting small-scale farmers. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.6990 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.69902025-12-28T12:10:49Z Implementing server-side federated learning in an edge-cloud framework for precision aquaculture Ng, Bryan Jing Hong S Agriculture (General) T Technology (General) TD Environmental technology. Sanitary engineering This project is about a growing need in the aquaculture industry which is precision aquaculture. Precision aquaculture involves the use of smart technologies such as sensors, cloud computing, and artificial intelligence to monitor and manage fish farming environments. However, small-scale farmers still face major challenges such as high implementation costs, poor internet connectivity, and concerns about data privacy. This project focuses on two key problems which are data privacy and system scalability. Most existing systems rely heavily on cloud connectivity and do not provide secure or efficient solutions for farms in remote areas with limited internet access. In this project, research and literature reviews were conducted to explore the current technologies in smart aquaculture, federated learning, and data privacy. Reviews include systems using IoT and AI-based models, along with analysis of different federated learning algorithms such as FedSGD, FedAvg, and FedProx, and privacypreserving methods such as DA, SA, HE and CKKS encryption. After identifying the gaps in current systems, this project proposes a secure and scalable server-side federated learning framework in an edge-cloud architecture for precision aquaculture. The system is designed to enable encrypted model training at the edge using IoT sensors and Raspberry Pi, while a federated learning server hosted on AWS aggregates updates without accessing raw data. The final product integrates edge computing, federated learning, encryption and cloud storage to create a privacy-preserving and cost-effective solution for monitoring aquaculture environments and supporting small-scale farmers. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6990/1/fyp_CS_2025_NBJH.pdf Ng, Bryan Jing Hong (2025) Implementing server-side federated learning in an edge-cloud framework for precision aquaculture. Final Year Project, UTAR. http://eprints.utar.edu.my/6990/ |
| spellingShingle | S Agriculture (General) T Technology (General) TD Environmental technology. Sanitary engineering Ng, Bryan Jing Hong Implementing server-side federated learning in an edge-cloud framework for precision aquaculture |
| title | Implementing server-side federated learning in an edge-cloud framework for precision aquaculture |
| title_full | Implementing server-side federated learning in an edge-cloud framework for precision aquaculture |
| title_fullStr | Implementing server-side federated learning in an edge-cloud framework for precision aquaculture |
| title_full_unstemmed | Implementing server-side federated learning in an edge-cloud framework for precision aquaculture |
| title_short | Implementing server-side federated learning in an edge-cloud framework for precision aquaculture |
| title_sort | implementing server-side federated learning in an edge-cloud framework for precision aquaculture |
| topic | S Agriculture (General) T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/6990/1/fyp_CS_2025_NBJH.pdf http://eprints.utar.edu.my/6990/ |
| url_provider | http://eprints.utar.edu.my |