Fast Deep Autoencoder for Federated learning
This addresses the need for efficient, privacy-preserving models in edge computing and federated learning, though it appears incremental as it modifies an existing approach.
The paper tackles the problem of slow training in deep autoencoders for federated learning by proposing DAEF, a non-iterative method that reduces training time while maintaining similar performance on anomaly detection datasets.
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.