Scalable Federated Learning for Clients with Different Input Image Sizes
  and Numbers of Output Categories
Shuhei Nitta Taiji Suzuki Albert Rodríguez Mulet Atsushi Yaguchi Ryusuke Hirai
Federated learning is a privacy-preserving training method which consists of
training from a plurality of clients but without sharing their confidential
data. However, previous work on federated learning do not explore suitable
neural network architectures for clients with different input images sizes and
different numbers of output categories. In this paper, we propose an effective
federated learning method named ScalableFL, where the depths and widths of the
local models for each client are adjusted according to the clients' input image
size and the numbers of output categories. In addition, we provide a new bound
for the generalization gap of federated learning. In particular, this bound
helps to explain the effectiveness of our scalable neural network approach. We
demonstrate the effectiveness of ScalableFL in several heterogeneous client
settings for both image classification and object detection tasks.