Title: Source-Target Unified Knowledge Distillation for Memory-Efficient Federated Domain Adaptation on Edge Devices. Abstract: To support local inference on an edge device, it is necessary to deploy a compact machine learning model on such a device.
When such a compact model is applied to a new environment, its inference accuracy can be degraded if the target data from the new environment have a different distribution from the source data that are used for model training.
To ensure high inference accuracy in the new environment, it is indispensable to adapt the compact model to the target data.
However, to protect users' privacy, the target data cannot be sent to a centralized server for joint training with the source data. Furthermore, utilizing the target data to directly train the compact model cannot achieve sufficient inference accuracy due to its low model capacity.
To this end, a scheme called source-target unified knowledge distillation (STU-KD) is developed in this paper. It starts with a large pretrained model loaded onto the edge device, and then the knowledge of the large model is transferred to the compact model via knowledge distillation.
Since training the large model leads to large memory consumption, a domain adaptation method called lite-residual hypothesis transfer is designed to achieve memory-efficient adaptation to the target data on the edge device. Moreover, to prevent the compact model from forgetting the knowledge of the source data during knowledge distillation, a collaborative knowledge distillation (Co-KD) method is developed to unify the source data on the server and the target data on the edge device to train the compact model. STU-KD can be easily integrated with secure aggregation so that the server cannot obtain the true model parameters of the compact model. Extensive experiments conducted upon several objective recognition tasks show that STU-KD can improve the inference accuracy by up to $14.7\%$, as compared to the state-of-the-art schemes. Results also reveal that the inference accuracy of the compact model is not impacted by incorporating secure aggregation into STU-KD.