边端学习如何在设备上高效、安全地运行AI模型
Onboard Optimization and Learning: A Survey
- 通过模型压缩与软硬件协同设计提升效率
- 实现低延迟、高隐私的本地实时推理与训练
- 适合需要自适应与强安全性的边缘智能场景
边端学习是边缘AI中的变革性方法，可在资源受限设备上直接进行实时数据处理、决策和模型自适应训练，无需依赖中心服务器。该范式对低延迟、高隐私和节能应用至关重要。然而，边端学习面临计算资源有限、推理成本高和安全漏洞等挑战。本综述系统梳理了多项应对策略，聚焦于模型效率优化、推理加速以及分布式设备间的协作学习。内容涵盖降低模型复杂度、提升推理速度、保障隐私计算的方法，以及增强动态环境下可扩展性和适应性的新兴技术。通过融合硬件-软件协同设计、模型压缩与去中心化学习进展，本综述揭示了边端学习的当前状态，为实现鲁棒、高效、安全的边缘AI部署提供洞见。
原文摘要 · Abstract (English)
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.
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