Federated learning (FL) is a commonly distributed algorithm for mobile users (MUs) training artificial intelligence (AI) models, however, several challenges arise when applying FL to real-world scenarios, such as label scarcity, non-IID data, and unexplainability. As a result, we propose an explainable personalized FL framework, called XPFL. First, we introduce a generative AI (GAI) assisted personalized federated semi-supervised learning, called GFed. Particularly, in local training, we utilize a GAI model to learn from large unlabeled data and apply knowledge distillation-based semi-supervised learning to train the local FL model using the knowledge acquired from the GAI model. In global aggregation, we obtain the new local FL model by fusing the local and global FL models in specific proportions, allowing each local model to incorporate knowledge from others while preserving its personalized characteristics. Second, we propose an explainable AI mechanism for FL, named XFed. Specifically, in local training, we apply a decision tree to match the input and output of the local FL model. In global aggregation, we utilize t-distributed stochastic neighbor embedding (t-SNE) to visualize the local models before and after aggregation. Finally, simulation results validate the effectiveness of the proposed XPFL framework.

翻译：联邦学习（FL）是一种常用于移动用户（MU）训练人工智能（AI）模型的分布式算法。然而，将FL应用于现实场景时面临诸多挑战，例如标签稀缺、非独立同分布数据以及不可解释性。为此，我们提出了一种可解释的个性化联邦学习框架，称为XPFL。首先，我们引入一种生成式人工智能（GAI）辅助的个性化联邦半监督学习方法，命名为GFed。具体而言，在本地训练中，我们利用GAI模型从大量无标签数据中学习，并应用基于知识蒸馏的半监督学习，利用从GAI模型获取的知识来训练本地FL模型。在全局聚合中，我们通过按特定比例融合本地与全局FL模型来获得新的本地FL模型，使得每个本地模型在保持其个性化特征的同时能够吸收其他模型的知识。其次，我们提出一种面向FL的可解释AI机制，称为XFed。具体来说，在本地训练中，我们应用决策树来匹配本地FL模型的输入与输出。在全局聚合中，我们利用t分布随机邻域嵌入（t-SNE）对聚合前后的本地模型进行可视化。最终，仿真结果验证了所提XPFL框架的有效性。