Heterogeneous Federated Learning: State-of-the-art and Research Challenges

Understand

Federated learning (FL) has drawn increasing attention owing to its potential use in large-scale industrial applications.

• Existing federated learning works mainly focus on model homogeneous settings.
• However, practical federated learning typically faces the heterogeneity of data distributions, model architectures, network environments, and hardware devices among participant clients.
• Heterogeneous Federated Learning (HFL) is much more challenging, and corresponding solutions are diverse and complex.