mlsanityData to 7 Oct 2026

Confidential Federated Computations

arXiv 2404.10764 ↗First posted 16 Apr 2024cs.CR cs.LGcited by 4

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Abstract (arXiv)

Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data. However, basic FLA systems have privacy limitations: they do not necessarily require anonymization mechanisms like differential privacy (DP), and provide limited protections against a potentially malicious service provider. Adding DP to a basic FLA system currently requires either adding excessive noise to each device's updates, or assuming an honest service provider that correctly implements the mechanism and only uses the privatized outputs. Secure multiparty computation (SMPC) -based oblivious aggregations can limit the service provider's access to individual user updates and improve DP tradeoffs, but the tradeoffs are still suboptimal, and they suffer from scalability challenges and susceptibility to Sybil attacks. This paper introduces a novel system architecture that leverages trusted execution environments (TEEs) and open-sourcing to both ensure confidentiality of server-side computations and provide externally verifiable privacy properties, bolstering the robustness and trustworthiness of private federated computations.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 67 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

22 of 67 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2019Towards Federated Learning at Scale: System Designcited by 826
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  5. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  6. 2021Practical and Private (Deep) Learning without Sampling or Shufflingcited by 169
  7. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 127
  8. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 126
  9. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 115
  10. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
  11. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
  12. 2021The Skellam Mechanism for Differentially Private Federated Learningcited by 66
10 more
  1. 2023Federated Learning of Gboard Language Models with Differential Privacycited by 64
  2. 2019Federated Learning Of Out-Of-Vocabulary Wordscited by 51
  3. 2023(Amplified) Banded Matrix Factorization: A unified approach to private trainingcited by 49
  4. 2020Training Keyword Spotting Models on Non-IID Data with Federated Learningcited by 36
  5. 2017Prio: Private, Robust, and Scalable Computation of Aggregate Statisticscited by 35
  6. 2015Simultaneous Private Learning of Multiple Conceptscited by 34
  7. 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
  8. 2024Elephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budgetcited by 5
  9. 2022Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Predictioncited by 4
  10. 2017Epsolute: Efficiently Querying Databases While Providing Differential Privacycited by 2

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Federated Learning in Practice: Reflections and Projectionscited by 15
  2. 2024A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTscited by 9
  3. 2025Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networkscited by 6
  4. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3