Autoencoder for Detecting Malicious Updates in Differentially Private Federated Learning

Differentially Private Federated Learning (DP-FL) is a novel machine learning paradigm that integrates federated learning with the principles of differential privacy. In DP-FL, a global model is trained across decentralized devices or servers, each holding local data samples, without the need to exchange raw data. This approach ensures data privacy by adding noise to the model updates before aggregation, thus preventing any individual contributor’s data from being compromised. However, ensuring the integrity of the model updates from these contributors is paramount. This research explores the application of autoencoders as a means to detect anomalous or fraudulent updates from contributors in DP-FL. By leveraging the reconstruction errors generated by autoencoders, this study assesses their effectiveness in identifying anomalies while also discussing potential limitations of this approach.