The increasing complexity of modern information systems necessitates adaptive frameworks capable of dynamic learning and real-time optimization. Traditional centralized architectures often struggle with scalability, security, and evolving computational demands, leading to inefficiencies in data-driven decision-making. In response, self-adaptive information systems have emerged as transformation approach, leveraging artificial intelligence (AI), federated learning, and decentralized optimization methods to enhance adaptability, efficiency, and security. This research explores the integration of self-adaptive systems with federated learning to improve decision-making while maintaining data privacy and security. Federated learning enables decentralized data processing across multiple nodes, ensuring privacy preservation without centralizing sensitive user information. Additionally, decentralized optimization techniques enhance the robustness and efficiency of system learning by distributing computational workloads and minimizing bottlenecks in large-scale environments. To validate these methodologies, we test federated learning and decentralized optimization on financial dataset containing attributes such as credit limits, demographic factors, payment history, and default risks. By training AI models on distributed nodes while optimizing learning rates and resource allocation, we aim to improve predictive accuracy in financial risk assessment while reducing computational overhead. Experimental results indicate that self-adaptive learning models outperform conventional centralized approaches, offering greater resilience in fluctuating data environments. The findings underscore the potential of self-adaptive information systems to enhance predictive capabilities, optimize resource allocation, and ensure secure financial data processing. This study provides foundation for further advancements in adaptive AI-driven financial systems, ensuring scalable, privacy-conscious, and efficient decision-making frameworks.