Federated Variational Inference: Towards Improved Personalization and Generalization
Federated Variational Inference: Towards Improved Personalization and Generalization
Conventional federated learning algorithms train a single global model by leveraging all participating clients’ data. However, due to heterogeneity in client generative distributions and predictive models, these approaches may not appropriately approximate the predictive process, converge to an optimal state, or generalize to new clients. We study personalization and generalization …