Federated Learning (FL) presents an innovative approach to privacy-preserving
distributed machine learning and enables efficient crowd intelligence on a
large scale. However, a significant challenge arises when coordinating FL with
crowd intelligence which diverse client groups possess disparate objectives due
to data heterogeneity or distinct tasks. To address this challenge, we propose
the Federated cINN Clustering Algorithm (FCCA) to robustly cluster clients into
different groups, avoiding mutual interference between clients with data
heterogeneity, and thereby enhancing the performance of the global model.
Specifically, FCCA utilizes a global encoder to transform each client's private
data into multivariate Gaussian distributions. It then employs a generative
model to learn encoded latent features through maximum likelihood estimation,
which eases optimization and avoids mode collapse. Finally, the central server
collects converged local models to approximate similarities between clients and
thus partition them into distinct clusters. Extensive experimental results
demonstrate FCCA's superiority over other state-of-the-art clustered federated
learning algorithms, evaluated on various models and datasets. These results
suggest that our approach has substantial potential to enhance the efficiency
and accuracy of real-world federated learning tasks.

我们提出了一种名为 FCCA 的联邦 cINN 聚类算法，通过将客户端的私有数据转换为多元高斯分布，学习编码的潜在特征，以达到客户端聚类和改善全局模型性能的目的。实验结果表明，FCCA 在各种模型和数据集上的性能优于其他最先进的集群联邦学习算法，表明我们的方法在提高现实世界中联邦学习任务的效率和准确性方面具有巨大的潜力。