Federated learning (FL) aims to collaboratively learn deep learning model
parameters from decentralized data archives (i.e., clients) without accessing
training data on clients. However, the training data across clients might be
not independent and identically distributed (non-IID), which may result in
difficulty in achieving optimal model convergence. In this work, we investigate
the capability of state-of-the-art transformer architectures (which are
MLP-Mixer, ConvMixer, PoolFormer) to address the challenges related to non-IID
training data across various clients in the context of FL for multi-label
classification (MLC) problems in remote sensing (RS). The considered
transformer architectures are compared among themselves and with the ResNet-50
architecture in terms of their: 1) robustness to training data heterogeneity;
2) local training complexity; and 3) aggregation complexity under different
non-IID levels. The experimental results obtained on the BigEarthNet-S2
benchmark archive demonstrate that the considered architectures increase the
generalization ability with the cost of higher local training and aggregation
complexities. On the basis of our analysis, some guidelines are derived for a
proper selection of transformer architecture in the context of FL for RS MLC.
The code of this work is publicly available at
this https URL

通过研究最先进的转换器架构（即 MLP-Mixer、ConvMixer、PoolFormer）在非独立和非同分布的训练数据上实现联合学习，针对遥感中的多标签分类问题，在不同非独立和非同分布程度下，比较了这些转换器架构在训练数据异质性、局部训练复杂性和聚合复杂性方面的性能，实验结果表明这些架构在增加泛化能力的同时增加了局部训练和聚合复杂性，为选择适用于遥感多标签分类领域的转换器架构提供了指导。