A Survey of Developments in Federated Meta-Learning

Federated meta-learning is a widely used few-shot learning method and has a very good development prospect. Federated meta-learning combines the characteristics of federated learning and meta-learning. It can not only use the data of each client while protecting its privacy to a certain extent, but also solve the problem of data volume that requires a large amount of data for model training in machine learning. With the rise of big data technology and edge computing, federated meta-learning technology has become a research hotspot in machine learning. In this paper, we provide an overview of the development of federated meta-learning and point out the relationship between federated learning, meta-learning and federated learning. Finally, some existing problems in federated meta-learning are pointed out, which provides ideas for the subsequent research on federated meta-learning.