System and method for artificial intelligence-based remediation of computing infrastructure issues using federated and reinforcement learning

Abstract

A system is provided for artificial intelligence-based remediation of computing infrastructure issues using federated and reinforcement learning. In particular, the system may use a federated learning strategy to monitor technical issues within the computing environment and identify patterns associated with the issues. Based on the patterns, the system may generate a rule template for remediating the issue. The system may further use a reinforcement learning strategy to orchestrate and execute the rules for remediating the issue on the affected computing devices or resources. The system may also use a fallback mechanism to ensure data and service availability while issues are being remediated. Local AI models may be installed on endpoint devices to support self-remediation, which in turn increases the computational efficiency of the remediation processes. In this way, the system provides an intelligent, efficient, and secure way to remediate issues within the computing environment.