Federated Variational Inference: Towards Improved Personalization and Generalization
Abstract: 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 in stateless cross-device federated learning setups assuming heterogeneity in client data distributions and predictive models. We first propose a hierarchical generative model and formalize it using Bayesian Inference. We then approximate this process using Variational Inference to train our model efficiently. We call this algorithm Federated Variational Inference (FedVI). We use PAC-Bayes analysis to provide generalization bounds for FedVI. We evaluate our model on FEMNIST and CIFAR-100 image classification and show that FedVI beats the state-of-the-art on both tasks.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: Submitted file includes a detailed point-by-point response for all the comments made by the reviewers as well as the updated manuscript with the high-lighted changes.
Assigned Action Editor: ~Antti_Honkela1
Submission Number: 2081
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