FedD2S: Personalized Data-Free Federated Knowledge Distillation
<|reference_start|>FedD2S: Personalized Data-Free Federated Knowledge Distillation: This paper addresses the challenge of mitigating data heterogeneity among clients within a Federated Learning (FL) framework. The model-drift issue, arising from the noniid nature of client data, often results in suboptimal personalization of a global model compared to locally trained models for each client. To tackle this challenge, we propose a novel approach named FedD2S for Personalized Federated Learning (pFL), leveraging knowledge distillation. FedD2S incorporates a deep-to-shallow layer-dropping mechanism in the data-free knowledge distillation process to enhance local model personalization. Through extensive simulations on diverse image datasets-FEMNIST, CIFAR10, CINIC0, and CIFAR100-we compare FedD2S with state-of-the-art FL baselines. The proposed approach demonstrates superior performance, characterized by accelerated convergence and improved fairness among clients. The introduced layer-dropping technique effectively captures personalized knowledge, resulting in enhanced performance compared to alternative FL models. Moreover, we investigate the impact of key hyperparameters, such as the participation ratio and layer-dropping rate, providing valuable insights into the optimal configuration for FedD2S. The findings demonstrate the efficacy of adaptive layer-dropping in the knowledge distillation process to achieve enhanced personalization and performance across diverse datasets and tasks.<|reference_end|>
@article{atapour2024fedd2s:,
    title={FedD2S: Personalized Data-Free Federated Knowledge Distillation},
    author={Kawa Atapour, S. Jamal Seyedmohammadi, Jamshid Abouei, Arash
  Mohammadi, Konstantinos N. Plataniotis},
    journal={arXiv preprint arXiv:2402.10846},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10846},
    primaryClass={cs.LG cs.AI cs.DC eess.IV}
}