Federated Learning (FL) is currently one of the most popular technologies in
the field of Artificial Intelligence (AI) due to its collaborative learning and
ability to preserve client privacy. However, it faces challenges such as
non-identically and non-independently distributed (non-IID) and data with
imbalanced labels among local clients. To address these limitations, the
research community has explored various approaches such as using local model
parameters, federated generative adversarial learning, and federated
representation learning. In our study, we propose a novel Clustered FedStack
framework based on the previously published Stacked Federated Learning
(FedStack) framework. The local clients send their model predictions and output
layer weights to a server, which then builds a robust global model. This global
model clusters the local clients based on their output layer weights using a
clustering mechanism. We adopt three clustering mechanisms, namely K-Means,
Agglomerative, and Gaussian Mixture Models, into the framework and evaluate
their performance. We use Bayesian Information Criterion (BIC) with the maximum
likelihood function to determine the number of clusters. The Clustered FedStack
models outperform baseline models with clustering mechanisms. To estimate the
convergence of our proposed framework, we use Cyclical learning rates.

基于已发布的堆叠联邦学习（FedStack）框架，本研究提出了一种新颖的聚类联邦堆叠（Clustered FedStack）框架，通过采用三种聚类机制，并结合贝叶斯信息准则（BIC）和循环学习率方法，能有效克服联邦学习中的数据分布不同和标签不平衡的问题，优于基准模型的性能。