# Secure Bayesian Federated Analytics for Privacy-Preserving Trend Detection

arXiv: https://arxiv.org/abs/2107.13640 · HTML: https://mlsanity.com/paper/2107.13640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 46 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Reinforcement Learning for Mention-Ranking Coreference Models](https://mlsanity.com/paper/1609.08667/) (2016)
- [Federated Learning Of Out-Of-Vocabulary Words](https://mlsanity.com/paper/1903.10635/) (2019)
- [Learning to Perform Physics Experiments via Deep Reinforcement Learning](https://mlsanity.com/paper/1611.01843/) (2016)
- [Federated Heavy Hitters Discovery with Differential Privacy](https://mlsanity.com/paper/1902.08534/) (2019)
- [A Comprehensive Analysis of Twitter Trending Topics](https://mlsanity.com/paper/1907.09007/) (2019)
- [Private Dataset Generation Using Privacy Preserving Collaborative Learning](https://mlsanity.com/paper/2004.13598/) (2020)

## Built on by (most cited first, 1 of 1)

- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)

## Abstract (arXiv)

Federated analytics has many applications in edge computing, its use can lead to better decision making for service provision, product development, and user experience. We propose a Bayesian approach to trend detection in which the probability of a keyword being trendy, given a dataset, is computed via Bayes' Theorem; the probability of a dataset, given that a keyword is trendy, is computed through secure aggregation of such conditional probabilities over local datasets of users. We propose a protocol, named SAFE, for Bayesian federated analytics that offers sufficient privacy for production grade use cases and reduces the computational burden of users and an aggregator. We illustrate this approach with a trend detection experiment and discuss how this approach could be extended further to make it production-ready.