Heterogeneous Federated Learning via Personalized Generative Networks
This addresses the challenge of data heterogeneity in Federated Learning for clients with empirical concept shifts, offering a novel approach to improve model convergence and generalization, though it is incremental as it builds on existing FL frameworks.
The paper tackles the problem of statistical heterogeneity in Federated Learning, which degrades performance and slows convergence, by proposing a method where the server trains client-specific generators to reduce conflicts between local models, supported by theoretical proof and experiments showing effectiveness in constructing a well-generalizable global model.
Federated Learning (FL) allows several clients to construct a common global machine-learning model without having to share their data. FL, however, faces the challenge of statistical heterogeneity between the client's data, which degrades performance and slows down the convergence toward the global model. In this paper, we provide theoretical proof that minimizing heterogeneity between clients facilitates the convergence of a global model for every single client. This becomes particularly important under empirical concept shifts among clients, rather than merely considering imbalanced classes, which have been studied until now. Therefore, we propose a method for knowledge transfer between clients where the server trains client-specific generators. Each generator generates samples for the corresponding client to remove the conflict with other clients' models. Experiments conducted on synthetic and real data, along with a theoretical study, support the effectiveness of our method in constructing a well-generalizable global model by reducing the conflict between local models.