# Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2112.10183 · HTML: https://mlsanity.com/paper/2112.10183/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2021.

## Built on by (most cited first, 5 of 5)

- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Can Decentralized Learning be more robust than Federated Learning?](https://mlsanity.com/paper/2303.03829/) (2023)
- [Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks](https://mlsanity.com/paper/2307.03197/) (2023)
- [Edge-Only Universal Adversarial Attacks in Distributed Learning](https://mlsanity.com/paper/2411.10500/) (2024)
- [Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning](https://mlsanity.com/paper/2507.07259/) (2025)

## Abstract (arXiv)

With the rapid demand of data and computational resources in deep learning systems, a growing number of algorithms to utilize collaborative machine learning techniques, for example, federated learning, to train a shared deep model across multiple participants. It could effectively take advantage of the resources of each participant and obtain a more powerful learning system. However, integrity and privacy threats in such systems have greatly obstructed the applications of collaborative learning. And a large amount of works have been proposed to maintain the model integrity and mitigate the privacy leakage of training data during the training phase for different collaborative learning systems. Compared with existing surveys that mainly focus on one specific collaborative learning system, this survey aims to provide a systematic and comprehensive review of security and privacy researches in collaborative learning. Our survey first provides the system overview of collaborative learning, followed by a brief introduction of integrity and privacy threats. In an organized way, we then detail the existing integrity and privacy attacks as well as their defenses. We also list some open problems in this area and opensource the related papers on GitHub: https://github.com/csl-cqu/awesome-secure-collebrative-learning-papers.