# Privacy-aware Berrut Approximated Coded Computing for Federated Learning

arXiv: https://arxiv.org/abs/2405.01704 · HTML: https://mlsanity.com/paper/2405.01704/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (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)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [Speeding Up Distributed Machine Learning Using Codes](https://mlsanity.com/paper/1512.02673/) (2015)
- [Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy](https://mlsanity.com/paper/1806.00939/) (2018)
- [SpArch: Efficient Architecture for Sparse Matrix Multiplication](https://mlsanity.com/paper/2002.08947/) (2020)
- [Precise Approximation of Convolutional Neural Networks for Homomorphically Encrypted Data](https://mlsanity.com/paper/2105.10879/) (2021)
- [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)
- [A Fundamental Tradeoff between Computation and Communication in Distributed Computing](https://mlsanity.com/paper/1604.07086/) (2016)
- [Private Retrieval, Computing and Learning: Recent Progress and Future Challenges](https://mlsanity.com/paper/2108.00026/) (2021)
- [Entangled Polynomial Codes for Secure, Private, and Batch Distributed Matrix Multiplication: Breaking the "Cubic" Barrier](https://mlsanity.com/paper/2001.05101/) (2020)
- [A Survey of Coded Distributed Computing](https://mlsanity.com/paper/2008.09048/) (2020)
- [Coded MapReduce](https://mlsanity.com/paper/1512.01625/) (2015)
- [Improving the condition number of estimated covariance matrices](https://mlsanity.com/paper/1810.10984/) (2018)
- [Private Polynomial Computation from Lagrange Encoding](https://mlsanity.com/paper/1812.04142/) (2018)

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

- [Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning](https://mlsanity.com/paper/2505.06759/) (2025)

## Abstract (arXiv)

Federated Learning (FL) is an interesting strategy that enables the collaborative training of an AI model among different data owners without revealing their private datasets. Even so, FL has some privacy vulnerabilities that have been tried to be overcome by applying some techniques like Differential Privacy (DP), Homomorphic Encryption (HE), or Secure Multi-Party Computation (SMPC). However, these techniques have some important drawbacks that might narrow their range of application: problems to work with non-linear functions and to operate large matrix multiplications and high communication and computational costs to manage semi-honest nodes. In this context, we propose a solution to guarantee privacy in FL schemes that simultaneously solves the previously mentioned problems. Our proposal is based on the Berrut Approximated Coded Computing, a technique from the Coded Distributed Computing paradigm, adapted to a Secret Sharing configuration, to provide input privacy to FL in a scalable way. It can be applied for computing non-linear functions and treats the special case of distributed matrix multiplication, a key primitive at the core of many automated learning tasks. Because of these characteristics, it could be applied in a wide range of FL scenarios, since it is independent of the machine learning models or aggregation algorithms used in the FL scheme. We provide analysis of the achieved privacy and complexity of our solution and, due to the extensive numerical results performed, a good trade-off between privacy and precision can be observed.