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A Scalable Federated Modular Neural Network Architecture for B5G-enabled Complex Systems
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  • Amina Djoudi ,
  • Younes Guellouma ,
  • Hadda Cherroun ,
  • Bouziane Brik
Amina Djoudi
Amar Telidji University of Laghouat

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Younes Guellouma
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Hadda Cherroun
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Bouziane Brik
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World applications could be optimized using AI approaches. However, due to the ever-increasing complexity of operations and services, it is challenging to address today's industrial systems' heterogeneous requirements for high reliability, ultra-low latency, high data rates, and limited resources through conventional neural networks. In this study, we suggest an AI-based modular architecture for handling an industrial human-made system, driven by Blockchain and 5G network slicing services. In order to successfully tackle the targeted issue by dividing it into subsystems based on AI and other methodologies, we aim to investigate a federated modular strategy powered by an intelligent storage system. The intelligent system removes human mistakes and uses AI to automate the system. This improves efficiency, and generalization capabilities regarding architecture and data. We examine a use case in the healthcare industry that has effectively demonstrated the advantages of our study.