Abstract:

The integration of 6G networks with Artificial Intelligence (AI) is poised to revolutionize the digital landscape by delivering ultra-low latency, massive connectivity, and intelligent automation. This transformation will enable futuristic applications such as holographic communication, digital twins, and real-time immersive experiences. However, current communication frameworks face several challenges including high latency, centralized data processing bottlenecks, and significant privacy concerns due to data aggregation in centralized AI models. These limitations hinder real-time performance and scalability in highly dynamic environments. To address these issues, we propose a novel framework based on Federated Learning (FL) across distributed 6G edge devices. FL enables collaborative model training directly on edge devices without the need to transfer raw data to a central server, thus preserving user privacy while reducing communication overhead. The proposed method harnesses the power of edge-AI integration, allowing devices to learn from local data while periodically aggregating model updates via a decentralized mechanism. This approach ensures adaptive learning, real-time decision-making, and resilient network performance across diverse 6G-enabled services. Experimental evaluations demonstrate that the FL-enabled 6G architecture significantly improves inference accuracy, reduces end-to-end latency, and enhances data privacy. The findings suggest that this method not only meets the demands of next-generation applications but also offers a sustainable path toward intelligent and secure network evolution.