mlsanityData to 14 Oct 2026

Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning

arXiv 2505.06759 ↗First posted 10 May 2025cs.LG cs.CR cs.DCcited by 1

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Abstract (arXiv)

Coded computing is one of the techniques that can be used for privacy protection in Federated Learning. However, most of the constructions used for coded computing work only under the assumption that the computations involved are exact, generally restricted to special classes of functions, and require quantized inputs. This paper considers the use of Private Berrut Approximate Coded Computing (PBACC) as a general solution to add strong but non-perfect privacy to federated learning. We derive new adapted PBACC algorithms for centralized aggregation, secure distributed training with centralized data, and secure decentralized training with decentralized data, thus enlarging significantly the applications of the method and the existing privacy protection tools available for these paradigms. Particularly, PBACC can be used robustly to attain privacy guarantees in decentralized federated learning for a variety of models. Our numerical results show that the achievable quality of different learning models (convolutional neural networks, variational autoencoders, and Cox regression) is minimally altered by using these new computing schemes, and that the privacy leakage can be bounded strictly to less than a fraction of one bit per participant. Additionally, the computational cost of the encoding and decoding processes depends only of the degree of decentralization of the data.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 38 references that are in the map. First posted on arXiv 10 May 2025.

Builds on

21 of 38 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  4. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  5. 2019Robust Aggregation for Federated Learningcited by 317
  6. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 175
  7. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  8. 2018Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacycited by 66
  9. 2017Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplicationcited by 32
  10. 2020Coded Computing for Low-Latency Federated Learning over Wireless Edge Networkscited by 32
  11. 2018On the Optimal Recovery Threshold of Coded Matrix Multiplicationcited by 27
  12. 2020Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computingcited by 19
9 more
  1. 2018CodedSketch: A Coding Scheme for Distributed Computation of Approximated Matrix Multiplicationcited by 16
  2. 2020Analog Lagrange Coded Computingcited by 13
  3. 2021Private Retrieval, Computing and Learning: Recent Progress and Future Challengescited by 12
  4. 2020A Survey of Coded Distributed Computingcited by 8
  5. 2018Private Polynomial Computation from Lagrange Encodingcited by 3
  6. 2023Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applicationscited by 3
  7. 2019Harmonic Coding: An Optimal Linear Code for Privacy-Preserving Gradient-Type Computationcited by 2
  8. 2020Coded Computing for Federated Learning at the Edgecited by 1
  9. 2024Privacy-aware Berrut Approximated Coded Computing for Federated Learningcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Privacy-Preserving and Verifiable Approximate Distributed Coded Computingcited by 0

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  4. 2023Binary Federated Learning with Client-Level Differential Privacy
  5. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
  6. 2024CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness
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