Abstract
Considering that Internet-of-Things (IoT) devices are often deployed in highly dynamic environments, mainly due to their continuous exposure to end-users’ living environments, it is imperative that the devices can continually learn new concepts from data stream without catastrophic forgetting. Although simply replaying all the previous training samples can alleviate this catastrophic forgetting problem, it not only may pose privacy risks, but also may require huge computing and memory resources, which makes this solution infeasible for resource-constrained IoT devices. In this paper, we propose IL4IoT, a lightweight framework for incremental learning for IoT devices. The framework consists of two cooperative parts: a continually updated knowledge-base and a task-solving model. Through this framework, we can achieve incremental learning while alleviating the catastrophic forgetting issue, without sacrificing privacy-protection and computing-resource efficiency. Our experiments on MNIST dataset and SDA dataset demonstrate the effectiveness and efficiency of our approach.
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Bao, Y., Chen, W. (2019). IL4IoT: Incremental Learning for Internet-of-Things Devices. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds) Ambient Intelligence. AmI 2019. Lecture Notes in Computer Science(), vol 11912. Springer, Cham. https://doi.org/10.1007/978-3-030-34255-5_7
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DOI: https://doi.org/10.1007/978-3-030-34255-5_7
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