Onboard learning is a transformative approach in edge AI, enabling real-time data processing, decision-making, and adaptive model training directly on resource-constrained devices without relying on centralized servers. This paradigm is crucial for applications demanding low latency, enhanced privacy, and energy efficiency. However, onboard learning faces challenges such as limited computational resources, high inference costs, and security vulnerabilities. This survey explores a comprehensive range of methodologies that address these challenges, focusing on techniques that optimize model efficiency, accelerate inference, and support collaborative learning across distributed devices. Approaches for reducing model complexity, improving inference speed, and ensuring privacy-preserving computation are examined alongside emerging strategies that enhance scalability and adaptability in dynamic environments. By bridging advancements in hardware-software co-design, model compression, and decentralized learning, this survey provides insights into the current state of onboard learning to enable robust, efficient, and secure AI deployment at the edge.

翻译：嵌入式学习是边缘人工智能领域的一种变革性方法，它能够在资源受限的设备上直接进行实时数据处理、决策制定和自适应模型训练，而无需依赖中心化服务器。这种范式对于需要低延迟、增强隐私保护和能效的应用至关重要。然而，嵌入式学习面临着计算资源有限、推理成本高以及安全漏洞等挑战。本综述探讨了应对这些挑战的多种方法，重点关注优化模型效率、加速推理以及支持分布式设备间协作学习的技术。在审视降低模型复杂度、提升推理速度和确保隐私保护计算等方法的同时，也探讨了增强动态环境中可扩展性与适应性的新兴策略。通过结合硬件-软件协同设计、模型压缩和去中心化学习等方面的进展，本综述深入剖析了嵌入式学习的现状，旨在为实现鲁棒、高效且安全的边缘人工智能部署提供见解。