Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings

• URL: http://arxiv.org/abs/2306.07959v1
• Date: Tue, 13 Jun 2023 17:55:30 GMT
• Title: Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings
• Authors: Disha Makhija and Joydeep Ghosh and Nhat Ho
• Abstract summary: This paper presents a unified federated learning framework based on customized local Bayesian models that learn well even in the absence of large local datasets. We use priors in the functional (output) space of the networks to facilitate collaboration across heterogeneous clients. Experiments on standard FL datasets demonstrate that our approach outperforms strong baselines in both homogeneous and heterogeneous settings.
• Score: 20.33482170846688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In several practical applications of federated learning (FL), the clients are highly heterogeneous in terms of both their data and compute resources, and therefore enforcing the same model architecture for each client is very limiting. Moreover, the need for uncertainty quantification and data privacy constraints are often particularly amplified for clients that have limited local data. This paper presents a unified FL framework to simultaneously address all these constraints and concerns, based on training customized local Bayesian models that learn well even in the absence of large local datasets. A Bayesian framework provides a natural way of incorporating supervision in the form of prior distributions. We use priors in the functional (output) space of the networks to facilitate collaboration across heterogeneous clients. Moreover, formal differential privacy guarantees are provided for this framework. Experiments on standard FL datasets demonstrate that our approach outperforms strong baselines in both homogeneous and heterogeneous settings and under strict privacy constraints, while also providing characterizations of model uncertainties.

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