In 6G mobile communication systems, various AI-based network functions and applications have been standardized. Federated learning (FL) is adopted as the core learning architecture for 6G systems to avoid privacy leakage from mobile user data. However, in FL, users with non-independent and identically distributed (non-IID) datasets can deteriorate the performance of the global model because the convergence direction of the gradient for each dataset is different, thereby inducing a weight divergence problem. To address this problem, we propose a novel diffusion strategy for machine learning (ML) models (FedDif) to maximize the performance of the global model with non-IID data. FedDif enables the local model to learn different distributions before parameter aggregation by passing the local models through users via device-to-device communication. Furthermore, we theoretically demonstrate that FedDif can circumvent the weight-divergence problem. Based on this theory, we propose a communication-efficient diffusion strategy for ML models that can determine the trade-off between learning performance and communication cost using auction theory. The experimental results show that FedDif improves the top-1 test accuracy by up to 34.89\% and reduces communication costs by 14.6% to a maximum of 63.49%.

翻译：在6G移动通信系统中，各种基于人工智能的网络功能和应用已实现标准化。联邦学习（FL）被采纳为6G系统的核心学习架构，以避免移动用户数据的隐私泄露。然而，在联邦学习中，拥有非独立同分布（non-IID）数据集的用户可能恶化全局模型的性能，因为每个数据集的梯度收敛方向不同，从而引发权重发散问题。为解决此问题，我们提出了一种新颖的机器学习（ML）模型扩散策略（FedDif），以最大化非独立同分布数据下全局模型的性能。FedDif通过设备到设备通信在用户间传递本地模型，使本地模型在参数聚合前学习不同的数据分布。此外，我们从理论上证明了FedDif能够规避权重发散问题。基于该理论，我们提出了一种通信高效的机器学习模型扩散策略，该策略可利用拍卖理论权衡学习性能与通信成本。实验结果表明，FedDif将Top-1测试准确率最高提升34.89%，并将通信成本降低14.6%至最高63.49%。