AIoT applications often encounter challenges such as terminal resource constraints, data drift, and data heterogeneity in real world, leading to problems such as catastrophic forgetting, low generalization ability, and low accuracy during model training. To address these challenges, we proposed CoLLaRS, a cloud–edge–terminal collaborative lifelong learning framework for AIoT applications. In the CoLLaRS framework, we alleviate the problem of terminal resource constraints by uploading terminal tasks at the edge. CoLLaRS uses continuous training at the edge to achieve lifelong learning training of the model and solve the problem of catastrophic forgetting. CoLLaRS employs federated optimization in the cloud to perform personalized aggregation of different edge models and solve the problem of weak model generalization ability. Finally, the model is fine-tuned at the terminal to further optimize its accuracy in local tasks. Our experiments on real-world datasets showed that CoLLaRS has an 8% improvement in accuracy and a 5% improvement in backward transfer(BWT) and forward transfer(FWT) compared to other baseline algorithms. The results of the ablation experiments further confirmed the effectiveness of CoLLaRS.

中文翻译：

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CoLLaRS：AIoT云边端协同终身学习框架


AIoT应用在现实世界中经常遇到终端资源限制、数据漂移、数据异构等挑战，导致模型训练时出现灾难性遗忘、泛化能力低、准确率低等问题。为了应对这些挑战，我们提出了CoLLaRS，一个面向AIoT应用的云边端协作终身学习框架。在CoLLaRS框架中，我们通过在边缘上传终端任务来缓解终端资源限制的问题。 CoLLaRS利用边缘持续训练的方式实现模型的终身学习训练，解决灾难性遗忘问题。 CoLLaRS采用云端联邦优化的方式，对不同边缘模型进行个性化聚合，解决模型泛化能力弱的问题。最后，模型在终端进行微调，进一步优化其在本地任务中的准确性。我们在真实数据集上的实验表明，与其他基线算法相比，CoLLaRS 的准确性提高了 8%，后向传输 (BWT) 和前向传输 (FWT) 提高了 5%。消融实验的结果进一步证实了CoLLaRS的有效性。