… a federated learning on a deep learning algorithm CNN based on model averaging. It is a
self-learning system for detecting anomalies … Federated learning applied for anomaly detection …

… Anomaly Detection (AD) in a centralized healthcare ecosystem is often plagued by … To
overcome these issues with centralized AD models, here we propose a Federated Learning (FL) …

… and missing detection issues for federated anomaly detection. 2) We propose to align the
definition of anomalies among clients by sharing noisy density functions for false detection. We …

… detection models may significantly decrease such risks as … of the usage of federated learning
for intrusion detection is its … and anomaly detection based on the federated learning, and …

… We apply Deep Autoencoder [17] as the model for anomaly detection to evaluate FL … a
federated learning algorithmic framework that utilizes adaptive optimizer and crossround learning …

… anomaly detection performance in FL, this study provides a separate summary and introduction
of the anomaly detection … of-the-art FL exception attack and detection in a non-IID case in …

… We evaluate our spectral anomaly detection approach against the image classification and
sentiment analysis tasks in the heterogeneous FL settings with various ML models, including …

… , machine learning models can also face resource limitations. Federated learning is a machine
learning-… Therefore, we propose a federated learning-based model incorporating the ZSM …

… for ECG analysis in federated settings that can … federated learning for two goals — to provide
enhanced data privacy and to reduce communication costs. The use of federated learning …

… on Multi-Task Feature Fusion Federated Learning (SADMC-MT-… learning framework based
on Multi-task Federated Learning (MT-FL) to construct multi-classification anomaly detection …