Asynchronous Real-Time Federated Learning for Anomaly Detection in Microservice Cloud Applications
The complexity and dynamicity of microservice architectures in cloud environments present substantial challenges to the reliability and availability of the services built on these architectures. Therefore, effective anomaly detection is crucial to prevent impending failures and resolve them promptly. Distributed data analysis techniques based on machine learning (ML) have recently gained attentionThe complexity and dynamicity of microservice architectures in cloud environments present substantial challenges to the reliability and availability of the services built on these architectures. Therefore, effective anomaly detection is crucial to prevent impending failures and resolve them promptly. Distributed data analysis techniques based on machine learning (ML) have recently gained attention
