AI-Driven Federated and Split Learning Frameworks for Optimized 6G Network Architectures

The next-generation 6G networks are expected to redefine global connectivity, demanding innovative solutions for data privacy, low-latency communications, and computational efficiency. This paper presents a novel architecture integrating Artificial Intelligence (AI) with 6 G systems, leveraging Federated Learning (FL), Split Learning (SL), and Low-Rank Adaptation (LoRA) techniques to optimize resource allocation and model training across distributed nodes. By employing FL and SL, I enhance privacy by minimizing the data transfer while enabling decentralized model training across edge devices. Additionally, LoRA is applied to fine-tune pre-trained models, significantly reducing the computational load and memory requirements of large-scale AI models in 6 G applications. The proposed solution addresses critical challenges, including model size, computational limitations, and network latency, providing an adaptable framework for the seamless integration of AI into 6G networks. This paper outlines the potential impact of these innovations on future communications infrastructures and presents theoretical evaluations of their effectiveness in improving 6G performance.