Abstract
To enhance the intelligence of IoT devices, offloading sufficient learning and inferencing down to the edge environment is promising. However, there are two main challenges for applying the cloud generated model in the edge environment. On the one hand, the input may vary on dimensions or cover different situations that the cloud hasn’t met. On the other hand, the model’s output might not satisfy the given user’s personalized preference. To make full use of the cloud generated model in the edge environment for accelerating personalized service provision, we propose cloud-aided edge learning. Unlike current federated learning and transfer learning, we focus on knowledge fusion in edge decision making and try to build the supplement/correction model. We take the personalized service provision in a smart lighting system as an example, design and implement the related deep reinforcement learning model, and take experiments based on the data generated on the open software DAILux to show our approach’s effectiveness and performance.
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Na, J., Zhang, H., Deng, X., Zhang, B., Ye, Z. (2020). Accelerate Personalized IoT Service Provision by Cloud-Aided Edge Reinforcement Learning: A Case Study on Smart Lighting. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_6
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