mlsanityData to 14 Oct 2026

Federated One-Shot Learning with Data Privacy and Objective-Hiding

arXiv 2504.21182 ↗First posted 29 Apr 2025cs.CR cs.DC cs.ITcited by 0

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Abstract (arXiv)

Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the clients. While the first aspect has been extensively studied, the second has received much less attention. We present a novel approach that addresses both concerns simultaneously, drawing inspiration from techniques in knowledge distillation and private information retrieval to provide strong information-theoretic privacy guarantees. Traditional private function computation methods could be used here; however, they are typically limited to linear or polynomial functions. To overcome these constraints, our approach unfolds in three stages. In stage 0, clients perform the necessary computations locally. In stage 1, these results are shared among the clients, and in stage 2, the federator retrieves its desired objective without compromising the privacy of the clients' data. The crux of the method is a carefully designed protocol that combines secret-sharing-based multi-party computation and a graph-based private information retrieval scheme. We show that our method outperforms existing tools from the literature when properly adapted to this setting.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 80 references that are in the map. First posted on arXiv 29 Apr 2025.

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30 of 80 references.

References · 30

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  5. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  6. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  7. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  8. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  9. 2021Adaptive Multi-Teacher Multi-level Knowledge Distillationcited by 72
  10. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 64
  11. 2019Unifying Heterogeneous Classifiers with Distillationcited by 25
  12. 2021CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learningcited by 20
18 more
  1. 2022SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learningcited by 18
  2. 2024Federated Distillation: A Surveycited by 15
  3. 2021Private Retrieval, Computing and Learning: Recent Progress and Future Challengescited by 12
  4. 2024Auditing Private Predictioncited by 7
  5. 2023Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusioncited by 5
  6. 2017Private Function Retrievalcited by 4
  7. 2020Semantic Private Information Retrievalcited by 4
  8. 2018Private Polynomial Computation from Lagrange Encodingcited by 3
  9. 2023Reducing the gap between streaming and non-streaming Transducer-based ASR by adaptive two-stage knowledge distillationcited by 3
  10. 2018Private Information Retrieval in Graph Based Replication Systemscited by 2
  11. 2022Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsificationcited by 2
  12. 2018Private Information Retrieval from Non-Replicated Databasescited by 1
  13. 2021Symmetric Private Information Retrieval with User-Side Common Randomnesscited by 1
  14. 2021Single-Server Private Linear Transformation: The Joint Privacy Casecited by 1
  15. 2021Multi-Server Private Linear Computation with Joint and Individual Privacy Guaranteescited by 1
  16. 2021Multi-Server Private Linear Transformation with Joint Privacycited by 1
  17. 2022Digital Blind Box: Random Symmetric Private Information Retrievalcited by 1
  18. 2023Privacy-Preserving Polynomial Computing Over Distributed Datacited by 1

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  2. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation
  3. 2023Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher
  4. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments
  5. 2025Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
  6. 2024Federated Learning Nodes Can Reconstruct Peers' Image Data
  7. 2024Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator
  8. 2024Privacy in Federated Learning