Privacy-Preserving Federated Deep Clustering based on GAN

Understand

Federated clustering (FC) is an essential extension of centralized clustering designed for the federated setting, wherein the challenge lies in constructing a global similarity measure without the need to share private data.

• Conventional approaches to FC typically adopt extensions of centralized methods, like K-means and fuzzy c-means.
• However, these methods are susceptible to non-independent-and-identically-distributed (non-IID) data among clients, leading to suboptimal performance, particularly with high-dimensional data.
• In this paper, we present a novel approach to address these limitations by proposing a Privacy-Preserving Federated Deep Clustering based on Generative Adversarial Networks (GANs).