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

arXiv: https://arxiv.org/abs/2505.06759 · HTML: https://mlsanity.com/paper/2505.06759/

## Key facts

- Cited by 1 papers 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 (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://mlsanity.com/paper/2002.04156/) (2020)
- [Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy](https://mlsanity.com/paper/1806.00939/) (2018)
- [Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication](https://mlsanity.com/paper/1705.10464/) (2017)
- [Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2011.06223/) (2020)
- [On the Optimal Recovery Threshold of Coded Matrix Multiplication](https://mlsanity.com/paper/1801.10292/) (2018)
- [Berrut Approximated Coded Computing: Straggler Resistance Beyond Polynomial Computing](https://mlsanity.com/paper/2009.08327/) (2020)
- [CodedSketch: A Coding Scheme for Distributed Computation of Approximated Matrix Multiplication](https://mlsanity.com/paper/1812.10460/) (2018)
- [Analog Lagrange Coded Computing](https://mlsanity.com/paper/2008.08565/) (2020)
- [Private Retrieval, Computing and Learning: Recent Progress and Future Challenges](https://mlsanity.com/paper/2108.00026/) (2021)
- [A Survey of Coded Distributed Computing](https://mlsanity.com/paper/2008.09048/) (2020)
- [Private Polynomial Computation from Lagrange Encoding](https://mlsanity.com/paper/1812.04142/) (2018)
- [Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications](https://mlsanity.com/paper/2307.11128/) (2023)
- [Harmonic Coding: An Optimal Linear Code for Privacy-Preserving Gradient-Type Computation](https://mlsanity.com/paper/1904.13206/) (2019)
- [Coded Computing for Federated Learning at the Edge](https://mlsanity.com/paper/2007.03273/) (2020)
- [Privacy-aware Berrut Approximated Coded Computing for Federated Learning](https://mlsanity.com/paper/2405.01704/) (2024)

## Built on by (most cited first, 1 of 1)

- [Privacy-Preserving and Verifiable Approximate Distributed Coded Computing](https://mlsanity.com/paper/2607.02187/) (2026)

## 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.