Learning from limited data has been extensively studied in machine learning,
considering that deep neural networks achieve optimal performance when trained
using a large amount of samples. Although various strategies have been proposed
for centralized training, the topic of federated learning with small datasets
remains largely unexplored. Moreover, in realistic scenarios, such as settings
where medical institutions are involved, the number of participating clients is
also constrained. In this work, we propose a novel federated learning
framework, named RepTreeFL. At the core of the solution is the concept of a
replica, where we replicate each participating client by copying its model
architecture and perturbing its local data distribution. Our approach enables
learning from limited data and a small number of clients by aggregating a
larger number of models with diverse data distributions. Furthermore, we
leverage the hierarchical structure of the client network (both original and
virtual), alongside the model diversity across replicas, and introduce a
diversity-based tree aggregation, where replicas are combined in a tree-like
manner and the aggregation weights are dynamically updated based on the model
discrepancy. We evaluated our method on two tasks and two types of data, graph
generation and image classification (binary and multi-class), with both
homogeneous and heterogeneous model architectures. Experimental results
demonstrate the effectiveness and outperformance of RepTreeFL in settings where
both data and clients are limited. Our code is available at
this https URL

在本研究中，我们提出了一种名为 RepTreeFL 的新型联邦学习框架，通过复制参与客户端的模型架构并扰动其本地数据分布的方式，在有限的数据和少数客户端的情况下，通过聚合多个具有不同数据分布的模型来实现学习。我们利用客户端网络的分层结构以及副本之间的模型差异性，引入了一种基于多样性的树聚合方法，在树状结构中组合副本，并根据模型差异性动态更新聚合权重。实验结果表明，在数据和客户端都有限的情况下，RepTreeFL 在图生成和图像分类（二分类和多分类）的两个任务和两种类型的数据中表现出良好的效果和优势。