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In today’s interconnected environment, it is crucial to strengthen network security in order to protect against constantly evolving threats. This study aims to contribute to this important goal by proposing a paradigm of collaborative federated learning as a method to improve security. The proposed framework, enhanced by the implementation of Federated Stochastic Gradient Descent (FedSGD), seeks to create reliable networks by attaining a desired accuracy level of 99.17%. The study involves a thorough investigation of the collaborative federated learning model, including its complex architecture and the smooth incorporation of FedSGD. The research explores various data sources and focuses on the complexities of preprocessing techniques to enhance model training. The user provides a clear explanation of the experimental setup, including the datasets, parameters, and configurations used for training. The chosen evaluation metrics guarantee a comprehensive evaluation of the model’s performance. The text presents the implementation details of collaborative federated learning deployment with FedSGD integration, explaining the complexities involved. The study proposes strategies to overcome potential obstacles that arise from the inherent challenges of implementing such an innovative approach. The achieved outcomes demonstrate the exceptional capability of the model, attaining an impressive accuracy of 99.17%. The model’s effectiveness in real-world scenarios and resilience against adversarial threats are supported by rigorous robustness and reliability analyses. The following discussion analyses the importance of the attained precision, elucidating its implications for the security of the network. Taking into account possible constraints, the research presents suggestions for future investigations and improvements. To summarize, this research strongly supports the implementation of collaborative federated learning with FedSGD as a crucial measure to establish reliable networks in response to current cybersecurity threats.

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Towards Trustworthy Networks: A Collaborative Federated Learning Paradigm for Security Enhancement

  • 会议论文

  • 发表日期：

    2025年

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  • Ritika Dhabliya,
  • R. Senthil Ganesh,
  • Nuzhat Rizvi,
  • Kshitiz Agarwal,
  • Gopal B. Deshmukh,
  • Sonali Prashant Dongare

摘要

In today’s interconnected environment, it is crucial to strengthen network security in order to protect against constantly evolving threats. This study aims to contribute to this important goal by proposing a paradigm of collaborative federated learning as a method to improve security. The proposed framework, enhanced by the implementation of Federated Stochastic Gradient Descent (FedSGD), seeks to create reliable networks by attaining a desired accuracy level of 99.17%. The study involves a thorough investigation of the collaborative federated learning model, including its complex architecture and the smooth incorporation of FedSGD. The research explores various data sources and focuses on the complexities of preprocessing techniques to enhance model training. The user provides a clear explanation of the experimental setup, including the datasets, parameters, and configurations used for training. The chosen evaluation metrics guarantee a comprehensive evaluation of the model’s performance. The text presents the implementation details of collaborative federated learning deployment with FedSGD integration, explaining the complexities involved. The study proposes strategies to overcome potential obstacles that arise from the inherent challenges of implementing such an innovative approach. The achieved outcomes demonstrate the exceptional capability of the model, attaining an impressive accuracy of 99.17%. The model’s effectiveness in real-world scenarios and resilience against adversarial threats are supported by rigorous robustness and reliability analyses. The following discussion analyses the importance of the attained precision, elucidating its implications for the security of the network. Taking into account possible constraints, the research presents suggestions for future investigations and improvements. To summarize, this research strongly supports the implementation of collaborative federated learning with FedSGD as a crucial measure to establish reliable networks in response to current cybersecurity threats.