mlsanityData to 7 Oct 2026

Federated Analytics: A survey

arXiv 2302.01326 ↗First posted 2 Feb 2023cs.LG cs.CRcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 30 of its 99 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

30 of 99 references.

References · 30

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2017Certified Defenses for Data Poisoning Attackscited by 288
  3. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  4. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  5. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
  6. 2019Secure Federated Matrix Factorizationcited by 102
  7. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  8. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 60
  9. 2016Discrete Distribution Estimation under Local Privacycited by 58
  10. 2015Local, Private, Efficient Protocols for Succinct Histogramscited by 35
  11. 2017Prio: Private, Robust, and Scalable Computation of Aggregate Statisticscited by 35
  12. 2019Federated Heavy Hitters Discovery with Differential Privacycited by 32
18 more
  1. 2017Practical Locally Private Heavy Hitterscited by 28
  2. 2018Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communicationcited by 26
  3. 2016Collecting and Analyzing Data from Smart Device Users with Local Differential Privacycited by 25
  4. 2020Secure Aggregation with Heterogeneous Quantization in Federated Learningcited by 21
  5. 2019Helen: Maliciously Secure Coopetitive Learning for Linear Modelscited by 20
  6. 2022How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?cited by 19
  7. 2021Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Datacited by 18
  8. 2015Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionariescited by 16
  9. 2022SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guaranteescited by 14
  10. 2020Lightweight Techniques for Private Heavy Hitterscited by 13
  11. 2019Communication Complexity in Locally Private Distribution Estimation and Heavy Hitterscited by 11
  12. 2019Conclave: secure multi-party computation on big data (extended TR)cited by 7
  13. 2016SMCQL: Secure Querying for Federated Databasescited by 5
  14. 2019PCKV: Locally Differentially Private Correlated Key-Value Data Collection with Optimized Utilitycited by 5
  15. 2020Senate: A Maliciously-Secure MPC Platform for Collaborative Analyticscited by 3
  16. 2021Prism: Private Verifiable Set Computation over Multi-Owner Outsourced Databasescited by 3
  17. 2017Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salarycited by 1
  18. 2019Secure Computation of the kth-Ranked Element in a Star Networkcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 1; 2025: 1; 2026: 2.

9 papers.

  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2023FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMscited by 19
  3. 2024Federated Learning in Practice: Reflections and Projectionscited by 15
  4. 2023Differentially Private Heavy Hitter Detection using Federated Analyticscited by 2
  5. 2023On the Fusion Strategies for Federated Decision Makingcited by 1
  6. 2023Holistic analysis on the sustainability of Federated Learning across AI product lifecyclecited by 0
  7. 2025Task-Agnostic Federation over Decentralized Data: Research Landscape and Visionscited by 0
  8. 2026FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systemscited by 0
  9. 2026A Simulated Federated Analysis of MS-Induced Brain Lesionscited by 0