# Differentially-Private Collaborative Online Personalized Mean Estimation

arXiv: https://arxiv.org/abs/2411.07094 · HTML: https://mlsanity.com/paper/2411.07094/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Federated Learning of a Mixture of Global and Local Models](https://mlsanity.com/paper/2002.05516/) (2020)
- [Decentralized Collaborative Learning of Personalized Models over Networks](https://mlsanity.com/paper/1610.05202/) (2016)
- [Federated Multi-armed Bandits with Personalization](https://mlsanity.com/paper/2102.13101/) (2021)
- [Distributed Exploration in Multi-Armed Bandits](https://mlsanity.com/paper/1311.0800/) (2013)
- [Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits](https://mlsanity.com/paper/1904.03293/) (2019)
- [One More Step Towards Reality: Cooperative Bandits with Imperfect Communication](https://mlsanity.com/paper/2111.12482/) (2021)
- [Parallel Best Arm Identification in Heterogeneous Environments](https://mlsanity.com/paper/2207.08015/) (2022)
- [Collaborative Algorithms for Online Personalized Mean Estimation](https://mlsanity.com/paper/2208.11530/) (2022)
- [Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy](https://mlsanity.com/paper/2303.00035/) (2023)
- [Scalable Decentralized Algorithms for Online Personalized Mean Estimation](https://mlsanity.com/paper/2402.12812/) (2024)

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

- [Communication-Constrained Private Decentralized Online Personalized Mean Estimation](https://mlsanity.com/paper/2511.04702/) (2025)

## Abstract (arXiv)

We consider the problem of collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. In particular, we provide a method based on hypothesis testing coupled with differential privacy and data variance estimation. Two privacy mechanisms and two data variance estimation schemes are proposed, and we provide a theoretical convergence analysis of the proposed algorithm for any bounded unknown distributions on the agents' data, showing that collaboration provides faster convergence than a fully local approach where agents do not share data. Moreover, we provide analytical performance curves for the case with an oracle class estimator, i.e., the class structure of the agents, where agents receiving data from distributions with the same mean are considered to be in the same class, is known. The theoretical faster-than-local convergence guarantee is backed up by extensive numerical results showing that for a considered scenario the proposed approach indeed converges much faster than a fully local approach, and performs comparably to ideal performance where all data is public. This illustrates the benefit of private collaboration in an online setting.