Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Privacy-Preserving Technologies in Data
Abstract
This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a framework where each device independently performs Bayesian inference on its local data and then adds noise to the updates based on differential privacy guarantees. This ensures that no single device's contribution can be identified, thus preserving privacy. The resulting system achieves accurate Bayesian inference across a decentralized network, offering a practical solution for privacy-sensitive data analysis. This work provides a new method for distributed learning which combines Bayesian inference, federated learning and differential privacy.
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