Profentzas, ChristosChristosProfentzasAlmgren, MagnusMagnusAlmgrenLandsiedel, OlafOlafLandsiedel2025-02-052025-02-052022-09Proceedings of the 47th IEEE Conference on Local Computer Networks: 1-8 (2022)978-1-6654-8001-7978-1-6654-8002-4https://hdl.handle.net/11420/53858Deep Neural Networks (DNNs) on IoT devices are becoming readily available for classification tasks using sensor data like images and audio. However, DNNs are trained using extensive computational resources such as GPUs on cloud services, and once being quantized and deployed on the IoT device remain unchanged. We argue in this paper, that this approach leads to three disadvantages. First, IoT devices are deployed in real-world scenarios where the initial problem may shift over time (e.g., to new or similar classes), but without re-training, DNNs cannot adapt to such changes. Second, IoT devices need to use energy-preserving communication with limited reliability and network bandwidth, which can delay or restrict the transmission of essential training sensor data to the cloud. Third, collecting and storing training sensor data in the cloud poses privacy concerns. A promising technique to mitigate these concerns is to utilize on-device Transfer Learning (TL). However, bringing TL to resource-constrained devices faces challenges and trade-offs in computational, energy, and memory constraints, which this paper addresses. This paper introduces MicroTL, Transfer Learning (TL) on low-power IoT devices. MicroTL tailors TL to IoT devices without the communication requirement with the cloud. Notably, we found that the MicroTL takes 3x less energy and 2.8x less time than transmitting all data to train an entirely new model in the cloud, showing that it is more efficient to retrain parts of an existing neural network on the IoT device.enIoT | Quantization | Transfer LearningMLE@TUHHTechnology::600: TechnologyConference Paper10.1109/LCN53696.2022.9843735Conference Paper