  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.
