# A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets

arXiv: https://arxiv.org/abs/2402.10145 · HTML: https://mlsanity.com/paper/2402.10145/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling](https://mlsanity.com/paper/1612.06573/) (2016)
- [HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography](https://mlsanity.com/paper/1909.05784/) (2019)
- [The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey](https://mlsanity.com/paper/1911.02621/) (2019)

## Built on by (most cited first, 3 of 3)

- [Blind Federated Learning without initial model](https://mlsanity.com/paper/2404.16180/) (2024)
- [Emerging Paradigms for Securing Federated Learning Systems](https://mlsanity.com/paper/2509.21147/) (2025)
- [Chaos-SSL: An Attention-Based Self-Supervised Learning Framework with Chaotic Transformation for Medical Image Classification](https://mlsanity.com/paper/2605.27146/) (2026)

## Abstract (arXiv)

Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a method for addressing the non-IID challenge. Moreover, differential privacy is compared with chaotic-based encryption as layer of privacy. The experimental approach assesses the performance of the federated deep learning model with differential privacy using both IID and non-IID data. In each experiment, the Federated Learning process improves the average performance metrics of the deep neural network, even in the case of non-IID data.