Federated Learning for Performance Anomaly Detection in Distributed Data Centers
Keywords:
federated learning, anomaly detection, data centers, performance monitoring, edge computing, CPU utilizationAbstract
The objective of this paper is to introduce the applications of Federation learning that may find performance issues in distributed data centres. Edge-resident models are used to learn CPU, memory, and I/O patterns baseline locally in real-time system while encrypted gradients reach to a central aggregator. Telemetry data and operational privacy are secured by this design architecture.
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