提出面向物联网的对比持续学习框架，解决设备动态适应与隐私保护难题。
Contrastive Continual Learning for Model Adaptability in Internet of Things
- 结合对比学习与知识蒸馏，设计抗遗忘模型更新机制
- 支持边缘与云端协同，在资源受限设备上实现高效持续学习
- 适合智能传感、边缘计算等低功耗物联网场景
物联网部署运行于非平稳、动态环境中，传感器漂移、用户行为演变及异构隐私需求会削弱应用效用。持续学习通过时间演进中无灾难性遗忘地更新模型来应对这一挑战。对比学习作为自监督表征学习范式，能提升鲁棒性与样本效率。本文综述了对比持续学习（CCL）在物联网中的应用，将算法设计（回放、正则化、知识蒸馏、提示工程）与系统现实（TinyML约束、间歇连接、隐私要求）相衔接。提出统一问题形式化，推导融合对比与蒸馏损失的通用目标函数；设计面向设备端、边缘与云的参考架构；提供评估协议与度量建议。最后指出物联网特有的开放挑战：涵盖表格与流式数据融合、概念漂移、联邦设置及能耗感知训练。
原文摘要 · Abstract (English)
Internet of Things (IoT) deployments operate in nonstationary, dynamic environments where factors such as sensor drift, evolving user behavior, and heterogeneous user privacy requirements can affect application utility. Continual learning (CL) addresses this by adapting models over time without catastrophic forgetting. Meanwhile, contrastive learning has emerged as a powerful representation-learning paradigm that improves robustness and sample efficiency in a self-supervised manner. This paper reviews the usage of \emph{contrastive continual learning} (CCL) for IoT, connecting algorithmic design (replay, regularization, distillation, prompts) with IoT system realities (TinyML constraints, intermittent connectivity, privacy). We present a unifying problem formulation, derive common objectives that blend contrastive and distillation losses, propose an IoT-oriented reference architecture for on-device, edge, and cloud-based CCL, and provide guidance on evaluation protocols and metrics. Finally, we highlight open unique challenges with respect to the IoT domain, such as spanning tabular and streaming IoT data, concept drift, federated settings, and energy-aware training.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。