Dataset Distillation-based Hybrid Federated Learning on Non-IID Data

• URL: http://arxiv.org/abs/2409.17517v2
• Date: Mon, 14 Jul 2025 00:42:10 GMT
• Title: Dataset Distillation-based Hybrid Federated Learning on Non-IID Data
• Authors: Xiufang Shi, Wei Zhang, Mincheng Wu, Guangyi Liu, Zhenyu Wen, Shibo He, Tejal Shah, Rajiv Ranjan,
• Abstract summary: We propose a hybrid federated learning framework called HFLDD, which integrates dataset distillation to generate independent and equally distributed (IID) data.<n>In particular, we partition the clients into heterogeneous clusters, where the data labels among different clients within a cluster are unbalanced.<n>This training process is like traditional federated learning on IID data, and hence effectively alleviates the impact of non-IID data on model training.
• Score: 18.226454363903446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of edge computing, Federated Learning (FL) has emerged as a promising solution for the intelligent Internet of Things (IoT). However, applying FL in mobile edge-cloud networks is greatly challenged by statistical heterogeneity and high communication overhead. To address it, we propose a hybrid federated learning framework called HFLDD, which integrates dataset distillation to generate approximately independent and equally distributed (IID) data, thereby improving the performance of model training. In particular, we partition the clients into heterogeneous clusters, where the data labels among different clients within a cluster are unbalanced while the data labels among different clusters are balanced. The cluster heads collect distilled data from the corresponding cluster members, and conduct model training in collaboration with the server. This training process is like traditional federated learning on IID data, and hence effectively alleviates the impact of non-IID data on model training. We perform a comprehensive analysis of the convergence behavior, communication overhead, and computational complexity of the proposed HFLDD. Extensive experimental results based on multiple public datasets demonstrate that when data labels are severely imbalanced, the proposed HFLDD outperforms the baseline methods in terms of both test accuracy and communication cost.

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