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<p>Recirculating aquaculture systems (RAS) represent a promising solution for sustainable fish production, but their commercial viability is hampered by a critical barrier: adapting intelligent control systems to new fish species requires extensive, species-specific data collection and lengthy retraining periods (45–60&#xa0;days). This challenge creates significant economic and operational hurdles for multi-species facilities, limiting their flexibility to adapt to market demands. This study addresses this fundamental limitation by introducing a novel framework that integrates transfer learning and federated intelligence to enable rapid, cost-effective, cross-species adaptation of deep reinforcement learning controllers. Building on our previous work with deep deterministic policy gradient (DDPG), we developed a modular neural architecture with species-agnostic and species-specific components. The system was validated across five distinct RAS configurations using three commercially important species: tilapia, rainbow trout, and European sea bass. The framework achieved 87.3% of optimal performance for a new species with just 14&#xa0;days of adaptation data, a dramatic improvement over traditional approaches. Furthermore, the federated learning implementation enabled continuous, privacy-preserving model improvement across multiple facilities, demonstrating a 23.5% collective performance improvement over individually trained systems. Economic analysis confirmed the framework’s commercial viability, with adaptation costs 76% lower than developing new species-specific systems and a projected return on investment of 4–14&#xa0;months. This research advances adaptive intelligent systems for aquaculture, offering a scalable and economically viable approach to precision RAS management. By significantly reducing implementation barriers, this work paves the way for wider commercial adoption, supporting the sustainable intensification required to meet global protein demands.</p>

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Enhanced transfer learning and federated intelligence for cross-species adaptability in intelligent recirculating aquaculture systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Ashwaq M. Alnemari,
  • Wael M. Elmessery,
  • Péter Szűcs,
  • Mohamed Hamdy Eid,
  • Wael Abdel-Moneim Omar,
  • Atef Fathy Ahmed,
  • Abdallah Elshawadfy Elwakeel

摘要

Recirculating aquaculture systems (RAS) represent a promising solution for sustainable fish production, but their commercial viability is hampered by a critical barrier: adapting intelligent control systems to new fish species requires extensive, species-specific data collection and lengthy retraining periods (45–60 days). This challenge creates significant economic and operational hurdles for multi-species facilities, limiting their flexibility to adapt to market demands. This study addresses this fundamental limitation by introducing a novel framework that integrates transfer learning and federated intelligence to enable rapid, cost-effective, cross-species adaptation of deep reinforcement learning controllers. Building on our previous work with deep deterministic policy gradient (DDPG), we developed a modular neural architecture with species-agnostic and species-specific components. The system was validated across five distinct RAS configurations using three commercially important species: tilapia, rainbow trout, and European sea bass. The framework achieved 87.3% of optimal performance for a new species with just 14 days of adaptation data, a dramatic improvement over traditional approaches. Furthermore, the federated learning implementation enabled continuous, privacy-preserving model improvement across multiple facilities, demonstrating a 23.5% collective performance improvement over individually trained systems. Economic analysis confirmed the framework’s commercial viability, with adaptation costs 76% lower than developing new species-specific systems and a projected return on investment of 4–14 months. This research advances adaptive intelligent systems for aquaculture, offering a scalable and economically viable approach to precision RAS management. By significantly reducing implementation barriers, this work paves the way for wider commercial adoption, supporting the sustainable intensification required to meet global protein demands.