mlsanityData to 7 Oct 2026

Fast Deep Autoencoder for Federated learning

arXiv 2206.05136 ↗First posted 10 Jun 2022cs.LGcited by 10

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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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 11 of its 47 references that are in the map. First posted on arXiv 10 Jun 2022.

Builds on

11 of 47 references.

References · 11

  1. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  2. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  3. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  4. 2018D"IoT: A Federated Self-learning Anomaly Detection System for IoTcited by 89
  5. 2022Variational Model Inversion Attackscited by 75
  6. 2013One-Class Classification: Taxonomy of Study and Review of Techniquescited by 68
  7. 2020A Federated Learning Approach to Anomaly Detection in Smart Buildingscited by 17
  8. 2018Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoTcited by 16
  9. 2020Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computingcited by 4
  10. 2020An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devicescited by 3
  11. 2021Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approachcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 0; 2026: 3.

10 papers.

  1. 2023Dynamic Heterogeneous Federated Learning with Multi-Level Prototypescited by 6
  2. 2024Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Datacited by 3
  3. 2024Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholdingcited by 2
  4. 2023Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?cited by 1
  5. 2024Federated t-SNE and UMAP for Distributed Data Visualizationcited by 1
  6. 2023Federated Battery Diagnosis and Prognosiscited by 0
  7. 2024Decentralised Traffic Incident Detection via Network Lassocited by 0
  8. 2026FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopycited by 0
  9. 2026Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detectioncited by 0
  10. 2026GCA: Global Centroid Alignment in Federated Learningcited by 0