  Federated Learning (FL) enables multiple clients to collaboratively learn a
machine learning model without exchanging their own local data. In this way,
the server can exploit the computational power of all clients and train the
model on a larger set of data samples among all clients. Although such a
mechanism is proven to be effective in various fields, existing works generally
assume that each client preserves sufficient data for training. In practice,
however, certain clients may only contain a limited number of samples (i.e.,
few-shot samples). For example, the available photo data taken by a specific
user with a new mobile device is relatively rare. In this scenario, existing FL
efforts typically encounter a significant performance drop on these clients.
Therefore, it is urgent to develop a few-shot model that can generalize to
clients with limited data under the FL scenario. In this paper, we refer to
this novel problem as federated few-shot learning. Nevertheless, the problem
remains challenging due to two major reasons: the global data variance among
clients (i.e., the difference in data distributions among clients) and the
local data insufficiency in each client (i.e., the lack of adequate local data
for training). To overcome these two challenges, we propose a novel federated
few-shot learning framework with two separately updated models and dedicated
training strategies to reduce the adverse impact of global data variance and
local data insufficiency. Extensive experiments on four prevalent datasets that
cover news articles and images validate the effectiveness of our framework
compared with the state-of-the-art baselines. Our code is provided at
https://github.com/SongW-SW/F2L.
