Prompt-Guided One-Shot Federated Learning via Synthetic Data Generation

Conventional Federated Learning (FL) requires the server to continuously establish wireless connections with clients to communicate learning models, which burdens the network in terms of management and wireless resource constraints, and may facilitate inference attacks. One-shot FL has emerged as a key innovation to reduce the framework's dependence on a consistent wireless connection. However, existing approaches largely treat clients as passive contributors: the algorithm determines how their local data are used, with limited support for heterogeneous user preferences. In this paper, we propose a one-shot FL framework that allows clients to actively control how their local data contribute to the learning process through synthetic data generation. Each client specifies its preference through a natural-language prompt, which guides generation together with its local images and controls how much the synthetic data deviate from the originals. In addition, we address the limited computational capability of client devices by incorporating a latent consistency model-enabled low-rank adaptation to boost the parameter and energy efficiency. Finally, we conduct a series of experiments to evaluate the effectiveness of our proposal against state-of-the-art benchmarks and obtain substantial accuracy improvements across different deep learning models and levels of heterogeneity across three datasets.