Self-Adaptive AI Systems Using Federated Learning and Autonomous Model Governance

The rapid adoption of Artificial Intelligence (AI) systems across distributed environments such as cloud platforms, edge devices, and Internet of Things (IoT) networks has raised serious concerns related to data privacy, security, fairness, and regulatory compliance. Traditional centralized machine learning approaches equire the aggregation of sensitive data into a single location, which is often impractical due to legal, ethical, and security constraints. Federated Learning (FL) has emerged as a promising decentralized learning paradigm that enables collaborative model training without sharing raw data. However, existing federated learning systems face several limitations, including model drift, bias accumulation, lack of adaptability to dynamic data distributions, and the absence of automated governance mechanisms. These challenges reduce long-term reliability and trustworthiness of AI systems deployed in real-world environments. This paper proposes a self-adaptive AI framework that integrates federated learning with autonomous model governance. The proposed system continuously monitors performance metrics, fairness indicators, and compliance constraints, and automatically adapts training strategies without human intervention. Autonomous governance mechanisms are embedded directly into the learning lifecycle to ensure ethical, transparent, and compliant AI behavior. The proposed approach improves robustness, scalability, and trustworthiness, making it suitable for privacy-sensitive and distributed applications.