mlsanityData to 7 Oct 2026

Secure Aggregation for Federated Learning in Flower

arXiv 2205.06117 ↗First posted 12 May 2022cs.LG cs.CRcited by 15

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Abstract (arXiv)

Federated Learning (FL) allows parties to learn a shared prediction model by delegating the training computation to clients and aggregating all the separately trained models on the server. To prevent private information being inferred from local models, Secure Aggregation (SA) protocols are used to ensure that the server is unable to inspect individual trained models as it aggregates them. However, current implementations of SA in FL frameworks have limitations, including vulnerability to client dropouts or configuration difficulties. In this paper, we present Salvia, an implementation of SA for Python users in the Flower FL framework. Based on the SecAgg(+) protocols for a semi-honest threat model, Salvia is robust against client dropouts and exposes a flexible and easy-to-use API that is compatible with various machine learning frameworks. We show that Salvia's experimental performance is consistent with SecAgg(+)'s theoretical computation and communication complexities.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 12 May 2022.

Builds on

12 of 31 references.

References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  6. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  7. 2021CrypTen: Secure Multi-Party Computation Meets Machine Learningcited by 131
  8. 2018A generic framework for privacy preserving deep learningcited by 103
  9. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  10. 2020FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learningcited by 60
  11. 2018Spectre Attacks: Exploiting Speculative Executioncited by 47
  12. 2020Communication-Computation Efficient Secure Aggregation for Federated Learningcited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 0; 2025: 6; 2026: 3.

15 papers.

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  1. 2022Machine Learning for Microcontroller-Class Hardware: A Reviewcited by 28
  2. 2022ScionFL: Efficient and Robust Secure Quantized Aggregationcited by 8
  3. 2022Protea: Client Profiling within Federated Systems using Flowercited by 4
  4. 2023CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communicationscited by 3
  5. 2025Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunitiescited by 2
  6. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  7. 2025Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacycited by 1
  8. 2025Differential Privacy-Driven Framework for Enhancing Heart Disease Predictioncited by 1
  9. 2025Distribution-Controlled Client Selection to Improve Federated Learning Strategiescited by 1
  10. 2023Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatmentcited by 0
  11. 2023MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflowscited by 0
  12. 2025Federated Foundation Language Model Post-Training Should Focus on Open-Source Modelscited by 0
3 more
  1. 2026PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systemscited by 0
  2. 2026FPLIER: Federated Pathway-Level Information Extractorcited by 0
  3. 2026Defining Decentralization: An Ontological Perspectivecited by 0