mlsanityData to 14 Oct 2026

GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning

arXiv 2410.08634 ↗First posted 11 Oct 2024cs.LG cs.IT math.ITcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 11 Oct 2024.

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References · 16

  1. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  4. 2021Model-Contrastive Federated Learningcited by 766
  5. 2021Towards Personalized Federated Learningcited by 464
  6. 2019On the Efficacy of Knowledge Distillationcited by 381
  7. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  8. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 100
  9. 2019Knowledge Distillation from Internal Representationscited by 87
  10. 2020Federated Mutual Learningcited by 71
  11. 2021SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Trainingcited by 45
  12. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
4 more
  1. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  2. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
  3. 2024Large Generative Model Assisted 3D Semantic Communicationcited by 12
  4. 2022Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learningcited by 6

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  8. 2024SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning