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Autonomous medical vehicles (AMVs) are revolutionizing emergency healthcare by enabling real-time patient monitoring, diagnostics, and decision-making. However, these systems face critical challenges such as data security, privacy, and efficient collaboration among distributed entities. This chapter proposes a novel blockchain-powered federated learning framework tailored for AMVs to address these issues. By integrating decentralized AI model training with blockchain, the framework ensures secure data sharing, robust privacy protection through advanced cryptographic techniques, and tamper-proof model updates. Smart contracts facilitate real-time model aggregation and incentivize data contribution, fostering collaboration between AMVs, hospitals, and medical networks. The proposed solution is scalable, leveraging lightweight consensus protocols, and optimized for latency-sensitive healthcare scenarios. This chapter also discusses potential applications, technical contributions, and challenges, paving the way for future advancements in autonomous medical systems.

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Blockchain-Driven Federated Learning Framework for Autonomous Medical Vehicles: Ensuring Privacy, Security, and Efficient AI Collaboration

  • 会议论文

  • 发表日期：

    2025年

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  • Chiang Liang Kok,
  • Jovan Bowen Heng

摘要

Autonomous medical vehicles (AMVs) are revolutionizing emergency healthcare by enabling real-time patient monitoring, diagnostics, and decision-making. However, these systems face critical challenges such as data security, privacy, and efficient collaboration among distributed entities. This chapter proposes a novel blockchain-powered federated learning framework tailored for AMVs to address these issues. By integrating decentralized AI model training with blockchain, the framework ensures secure data sharing, robust privacy protection through advanced cryptographic techniques, and tamper-proof model updates. Smart contracts facilitate real-time model aggregation and incentivize data contribution, fostering collaboration between AMVs, hospitals, and medical networks. The proposed solution is scalable, leveraging lightweight consensus protocols, and optimized for latency-sensitive healthcare scenarios. This chapter also discusses potential applications, technical contributions, and challenges, paving the way for future advancements in autonomous medical systems.