# Federated Analytics: A survey

arXiv: https://arxiv.org/abs/2302.01326 · HTML: https://mlsanity.com/paper/2302.01326/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 99 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 30 of 30)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Certified Defenses for Data Poisoning Attacks](https://mlsanity.com/paper/1706.03691/) (2017)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [FairFed: Enabling Group Fairness in Federated Learning](https://mlsanity.com/paper/2110.00857/) (2021)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2109.14236/) (2021)
- [FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2009.11248/) (2020)
- [Discrete Distribution Estimation under Local Privacy](https://mlsanity.com/paper/1602.07387/) (2016)
- [Local, Private, Efficient Protocols for Succinct Histograms](https://mlsanity.com/paper/1504.04686/) (2015)
- [Prio: Private, Robust, and Scalable Computation of Aggregate Statistics](https://mlsanity.com/paper/1703.06255/) (2017)
- [Federated Heavy Hitters Discovery with Differential Privacy](https://mlsanity.com/paper/1902.08534/) (2019)
- [Practical Locally Private Heavy Hitters](https://mlsanity.com/paper/1707.04982/) (2017)
- [Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication](https://mlsanity.com/paper/1802.04705/) (2018)
- [Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy](https://mlsanity.com/paper/1606.05053/) (2016)
- [Helen: Maliciously Secure Coopetitive Learning for Linear Models](https://mlsanity.com/paper/1907.07212/) (2019)
- [Secure Aggregation with Heterogeneous Quantization in Federated Learning](https://mlsanity.com/paper/2009.14388/) (2020)
- [How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?](https://mlsanity.com/paper/2208.02304/) (2022)
- [Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data](https://mlsanity.com/paper/2105.08925/) (2021)
- [Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries](https://mlsanity.com/paper/1503.01214/) (2015)
- [SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees](https://mlsanity.com/paper/2202.04169/) (2022)
- [Lightweight Techniques for Private Heavy Hitters](https://mlsanity.com/paper/2012.14884/) (2020)
- [Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters](https://mlsanity.com/paper/1905.11888/) (2019)
- [Conclave: secure multi-party computation on big data (extended TR)](https://mlsanity.com/paper/1902.06288/) (2019)
- [PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utility](https://mlsanity.com/paper/1911.12834/) (2019)
- [SMCQL: Secure Querying for Federated Databases](https://mlsanity.com/paper/1606.06808/) (2016)
- [Senate: A Maliciously-Secure MPC Platform for Collaborative Analytics](https://mlsanity.com/paper/2010.13752/) (2020)
- [Prism: Private Verifiable Set Computation over Multi-Owner Outsourced Databases](https://mlsanity.com/paper/2104.03354/) (2021)
- [Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary](https://mlsanity.com/paper/1703.09845/) (2017)
- [Secure Computation of the kth-Ranked Element in a Star Network](https://mlsanity.com/paper/1909.08347/) (2019)

## Built on by (most cited first, 9 of 9)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs](https://mlsanity.com/paper/2306.04959/) (2023)
- [Federated Learning in Practice: Reflections and Projections](https://mlsanity.com/paper/2410.08892/) (2024)
- [Differentially Private Heavy Hitter Detection using Federated Analytics](https://mlsanity.com/paper/2307.11749/) (2023)
- [On the Fusion Strategies for Federated Decision Making](https://mlsanity.com/paper/2303.06109/) (2023)
- [Holistic analysis on the sustainability of Federated Learning across AI product lifecycle](https://mlsanity.com/paper/2312.14628/) (2023)
- [Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions](https://mlsanity.com/paper/2503.03140/) (2025)
- [FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems](https://mlsanity.com/paper/2602.01185/) (2026)
- [A Simulated Federated Analysis of MS-Induced Brain Lesions](https://mlsanity.com/paper/2605.08223/) (2026)

## Abstract (arXiv)

Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g., hospitals, banks) without sharing the data among parties. Motivated by the practical use cases of federated analytics, we follow a systematic discussion on federated analytics in this article. In particular, we discuss the unique characteristics of federated analytics and how it differs from federated learning. We also explore a wide range of FA queries and discuss various existing solutions and potential use case applications for different FA queries.