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The demand for secure, scalable, and low-latency data management solutions has intensified the integration of 5G networks, Edge computing, and Internet of Things (IoT)-driven technologies. Modern systems, characterized by distributed devices and sensitive data, face privacy, interoperability, and resource optimization challenges. Traditional centralized approaches struggle to address these issues due to vulnerabilities in security, high communication overhead, and regulatory constraints. This chapter explores integrating BC and Federated Learning (FL) technologies as a transformative paradigm to manage network slices in 5G, Fog, Edge, and Cloud environments, focusing on healthcare applications. Blockchain (BC) offers decentralized, tamper-proof data storage and Transaction (Tx) validation through consensus mechanisms like Proof of Work (PoW) and Proof of Stake (PoS). At the same time, FL enables collaborative Machine Learning (ML) without centralized data aggregation. Together, they form FedBLOC—a hybrid architecture that enhances trust, security, and efficiency in distributed systems. The paper systematically examines BC fundamentals, FL algorithms, and network slicing in 5G, highlighting their synergistic potential. Network slicing, which creates virtualized, application-specific networks, benefits from BC’s immutability and FL’s privacy-preserving model training, enabling real-time healthcare services like remote patient monitoring and Artificial Intelligence (AI)-driven diagnostics. By addressing challenges such as data security, incentive mechanisms, and system resilience, the integration of these technologies paves the way for robust, decentralized healthcare ecosystems. The chapter also discusses practical implementations, including secure medical record sharing, federated AI models for disease detection, and dynamic resource allocation in smart hospitals. This work underscores the critical role of BC and FL in advancing secure, efficient, and scalable solutions for next-generation networks and healthcare systems.

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BC and Federated Learning for the Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds

  • 章节

  • 发表日期：

    2026年

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  • Richa Golash,
  • Shahnawaz Ahmad,
  • Bhawana,
  • Naween Kumar

摘要

The demand for secure, scalable, and low-latency data management solutions has intensified the integration of 5G networks, Edge computing, and Internet of Things (IoT)-driven technologies. Modern systems, characterized by distributed devices and sensitive data, face privacy, interoperability, and resource optimization challenges. Traditional centralized approaches struggle to address these issues due to vulnerabilities in security, high communication overhead, and regulatory constraints. This chapter explores integrating BC and Federated Learning (FL) technologies as a transformative paradigm to manage network slices in 5G, Fog, Edge, and Cloud environments, focusing on healthcare applications. Blockchain (BC) offers decentralized, tamper-proof data storage and Transaction (Tx) validation through consensus mechanisms like Proof of Work (PoW) and Proof of Stake (PoS). At the same time, FL enables collaborative Machine Learning (ML) without centralized data aggregation. Together, they form FedBLOC—a hybrid architecture that enhances trust, security, and efficiency in distributed systems. The paper systematically examines BC fundamentals, FL algorithms, and network slicing in 5G, highlighting their synergistic potential. Network slicing, which creates virtualized, application-specific networks, benefits from BC’s immutability and FL’s privacy-preserving model training, enabling real-time healthcare services like remote patient monitoring and Artificial Intelligence (AI)-driven diagnostics. By addressing challenges such as data security, incentive mechanisms, and system resilience, the integration of these technologies paves the way for robust, decentralized healthcare ecosystems. The chapter also discusses practical implementations, including secure medical record sharing, federated AI models for disease detection, and dynamic resource allocation in smart hospitals. This work underscores the critical role of BC and FL in advancing secure, efficient, and scalable solutions for next-generation networks and healthcare systems.