COGNITIVE-AI-DRIVEN MULTIMODAL COMPUTING FRAMEWORK FOR REAL-TIME SEMANTIC REASONING IN DISTRIBUTED INTELLIGENT SYSTEMS: A CONTEXT-AWARE ARCHITECTURE COMBINING TRANSFORMER MODELS, KNOWLEDGE GRAPHS, AND FEDERATED LEARNING FOR SCALABLE DECISION SUPPORT

The emergence of distributed intelligent systems has necessitated robust, scalable frameworks capable of real-time semantic reasoning. This paper proposes a novel Cognitive-AI-driven Multimodal Computing Framework that integrates Transformer models, knowledge graphs, and federated learning into a unified, context-aware architecture. The objective is to enhance semantic understanding and decision-making capabilities across decentralized environments such as smart cities, edge IoT networks, and autonomous agents. Leveraging the representational power of Transformers, the ontological precision of knowledge graphs, and the privacy-preserving strengths of federated learning, this framework delivers scalable, real-time, and interpretable inference. Experiments conducted on synthetic and real-world multimodal datasets demonstrate significant improvements in semantic accuracy, latency, and decision reliability.