Few-shot learning in wireless networks: a meta-learning model-enabled scheme

K Xiong, Z Zhao, W Hong, M Peng… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
2022 IEEE International Conference on Communications Workshops …, 2022ieeexplore.ieee.org
Restricted by the data sensing capability, it is challenging for a single user to generate high-
quality deep learning models based on its collected few-shot data samples. Meta-learning
provides a promising paradigm to make full use of historical data at the base stations to
improve the performance of few-shot learning tasks. However, it is a dilemma to balance the
performance and the communication costs of meta-learning. In this paper, we studied the
design of few-shot learning in wireless networks. First, a meta-learning model-based …
Restricted by the data sensing capability, it is challenging for a single user to generate high-quality deep learning models based on its collected few-shot data samples. Meta-learning provides a promising paradigm to make full use of historical data at the base stations to improve the performance of few-shot learning tasks. However, it is a dilemma to balance the performance and the communication costs of meta-learning. In this paper, we studied the design of few-shot learning in wireless networks. First, a meta-learning model-based scheme is designed to adapt the few-shot learning tasks, and a multicasting-based model transmission scheme is proposed. Second, a coalition formation-based model selection scheme is designed to achieve a sophisticated tradeoff between the performance and the communication costs of meta-learning. Finally, the simulation results are provided, which show that our proposed scheme can improve the model accuracy performance with low communication costs.
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