The increased adoption of intelligent devices and the development of 6 G networks have resulted in a tremendous amount of edge data being generated in a distributed fashion, thus giving rise to a clear need for privacy-preserving and communication-efficient learning. Federated learning (FL) enables shared model learning, but it has some limitations, such as high communication overhead due to non-IID data distribution, device straggling, and inflexible offloading strategies in wireless communication networks. The existing research addressed all the above-mentioned issues related to traditional federated learning either through device-to-device communication methods for 6 G networks or through edge server-based strategies for wireless communication networks. However, the existing research could not improve both communication efficiency and learning performance simultaneously. In this research, an adaptive offloading federated learning framework for D2D-enabled 6G networks is proposed that leverages the strengths of both D2D communication methods and edge server-based offloading strategies. The proposed framework integrates channel-state awareness with weighted federated averaging to enable stable global aggregation under non-IID data distributions. By incorporating communication constraints into the learning process, it jointly improves convergence behaviour and transmission efficiency. Experimental validation on the MNIST benchmark demonstrates robust convergence with reduced communication overhead across federated rounds. The adaptive hybrid strategy, combining device-to-device collaboration and edge offloading, enhances scalability and resource efficiency.