# Fast Deep Autoencoder for Federated learning

arXiv: https://arxiv.org/abs/2206.05136 · HTML: https://mlsanity.com/paper/2206.05136/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [Variational Model Inversion Attacks](https://mlsanity.com/paper/2201.10787/) (2022)
- [One-Class Classification: Taxonomy of Study and Review of Techniques](https://mlsanity.com/paper/1312.0049/) (2013)
- [A Federated Learning Approach to Anomaly Detection in Smart Buildings](https://mlsanity.com/paper/2010.10293/) (2020)
- [Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT](https://mlsanity.com/paper/1812.04872/) (2018)
- [Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing](https://mlsanity.com/paper/2001.03314/) (2020)
- [An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices](https://mlsanity.com/paper/2002.12301/) (2020)
- [Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach](https://mlsanity.com/paper/2108.03872/) (2021)

## Built on by (most cited first, 10 of 10)

- [Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes](https://mlsanity.com/paper/2312.09881/) (2023)
- [Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data](https://mlsanity.com/paper/2404.14933/) (2024)
- [Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding](https://mlsanity.com/paper/2410.09284/) (2024)
- [Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?](https://mlsanity.com/paper/2311.04948/) (2023)
- [Federated t-SNE and UMAP for Distributed Data Visualization](https://mlsanity.com/paper/2412.13495/) (2024)
- [Federated Battery Diagnosis and Prognosis](https://mlsanity.com/paper/2310.09628/) (2023)
- [Decentralised Traffic Incident Detection via Network Lasso](https://mlsanity.com/paper/2402.18167/) (2024)
- [FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy](https://mlsanity.com/paper/2603.19512/) (2026)
- [Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection](https://mlsanity.com/paper/2608.08906/) (2026)
- [GCA: Global Centroid Alignment in Federated Learning](https://mlsanity.com/paper/2608.22593/) (2026)

## Abstract (arXiv)

This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep Autoencoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network in a non-iterative way, which drastically reduces its training time. Its training can be carried out in a distributed way (several partitions of the dataset in parallel) and incrementally (aggregation of partial models), and due to its mathematical formulation, the data that is exchanged does not endanger the privacy of the users. This makes DAEF a valid method for edge computing and federated learning scenarios. The method has been evaluated and compared to traditional (iterative) deep autoencoders using seven real anomaly detection datasets, and their performance have been shown to be similar despite DAEF's faster training.