The integration of federated learning and zero-trust security offers a promising solution for enhancing wireless communication security. This comprehensive exploration examines the distinct functionalities of these methodologies and their synergistic potential in fortifying security measures. Given the escalating complexity of cyber threats, there is an urgent need for robust, adaptable security frameworks, a requirement that can be addressed by this innovative combination. By leveraging the decentralized data processing capabilities of federated learning and the comprehensive security controls of zero-trust models, resistance against potential breaches can be significantly bolstered. The work also acknowledges and proposes solutions for inherent challenges in the implementation. The conclusion emphasizes the immense potential of this synergy to revolutionize wireless communication security, providing a robust platform for future research.

中文翻译：

---

用于安全无线通信的集成联合学习和零信任方法

联邦学习和零信任安全的集成为增强无线通信安全性提供了一种有前景的解决方案。这项全面的探索研究了这些方法的独特功能及其在强化安全措施方面的协同潜力。鉴于网络威胁的复杂性不断升级，迫切需要强大、适应性强的安全框架，这一创新组合可以满足这一要求。通过利用联邦学习的去中心化数据处理能力和零信任模型的全面安全控制，可以显着增强对潜在违规行为的抵抗力。这项工作还承认并提出了实施过程中固有挑战的解决方案。结论强调了这种协同作用在彻底改变无线通信安全方面的巨大潜力，为未来的研究提供了强大的平台。