标题： 网络边缘计算中的鲁棒联邦学习：最新进展与未来趋势 显示英文标题

标题： Towards Resilient Federated Learning in CyberEdge Networks: Recent Advances and Future Trends

摘要： 在这篇综述中，我们调查了网络边缘计算环境中弹性联邦学习（ResFL）的最新技术，重点关注联合训练与聚类推理和面向特征的安全机制。 我们探讨自适应分层学习策略以应对非独立同分布（non-IID）数据的挑战，从而提高可扩展性并减少通信开销。 研究了容错技术和聚类推理机制，用以检测不可靠设备、优化模型更新并增强收敛稳定性。 与现有的联邦学习安全研究不同，我们全面分析了面向特征的威胁，如利用模型特征的投毒攻击、推理攻击和重建攻击。 此外，我们还考察了弹性聚合技术、异常检测以及包括差分隐私和安全多方计算在内的加密防御措施，以加强联邦学习的安全性。 另外，我们讨论了将6G、大规模语言模型（LLMs）和互操作学习框架相结合，以提升隐私保护和去中心化跨域训练的能力。 这些进展提供了超低延迟、人工智能（AI）驱动的网络管理，并增强了对对抗性攻击的抵御能力，促进了在网络安全边缘网络中部署安全的ResFL。 显示英文摘要

摘要： In this survey, we investigate the most recent techniques of resilient federated learning (ResFL) in CyberEdge networks, focusing on joint training with agglomerative deduction and feature-oriented security mechanisms. We explore adaptive hierarchical learning strategies to tackle non-IID data challenges, improving scalability and reducing communication overhead. Fault tolerance techniques and agglomerative deduction mechanisms are studied to detect unreliable devices, refine model updates, and enhance convergence stability. Unlike existing FL security research, we comprehensively analyze feature-oriented threats, such as poisoning, inference, and reconstruction attacks that exploit model features. Moreover, we examine resilient aggregation techniques, anomaly detection, and cryptographic defenses, including differential privacy and secure multi-party computation, to strengthen FL security. In addition, we discuss the integration of 6G, large language models (LLMs), and interoperable learning frameworks to enhance privacy-preserving and decentralized cross-domain training. These advancements offer ultra-low latency, artificial intelligence (AI)-driven network management, and improved resilience against adversarial attacks, fostering the deployment of secure ResFL in CyberEdge networks.