GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning

• URL: http://arxiv.org/abs/2410.08634v1
• Date: Fri, 11 Oct 2024 08:58:05 GMT
• Title: GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning
• Authors: Yubo Peng, Feibo Jiang, Li Dong, Kezhi Wang, Kun Yang,
• Abstract summary: Federated learning (FL) is a commonly distributed algorithm for mobile users (MUs) training artificial intelligence (AI) models. We propose an explainable personalized FL framework, called XPFL. Particularly, in local training, we utilize a generative AI (GAI) model to learn from large unlabeled data. In global aggregation, we obtain the new local local model by fusing the local and global FL models in specific proportions. Finally, simulation results validate the effectiveness of the proposed XPFL framework.
• Score: 29.931169585178818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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