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A Knowledge-Imparting Generative Modelling Framework for Heterogeneous Federated Learning

Hongyao Chen Tianyang Xu Xiaojun Wu Josef Kittler

自动化学报（英文版）2026，Vol.13Issue(8)：1938-1951,14.

自动化学报（英文版）2026，Vol.13Issue(8)：1938-1951,14.DOI:10.1109/JAS.2026.125840

A Knowledge-Imparting Generative Modelling Framework for Heterogeneous Federated Learning

Hongyao Chen ¹Tianyang Xu ¹Xiaojun Wu ¹Josef Kittler²

作者信息

1. School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,China
2. School of Computer Science and Electronic Engineering and the Centre for Vision,Speech and Signal Processing(CVSSP),University of Surrey,Guildford GU2 7XH,UK
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摘要

关键词

Adversarial training/federated learning(FL)/gen-erative models/knowledge distillation

Key words

Adversarial training/federated learning(FL)/gen-erative models/knowledge distillation

引用本文复制引用

Hongyao Chen,Tianyang Xu,Xiaojun Wu,Josef Kittler..A Knowledge-Imparting Generative Modelling Framework for Heterogeneous Federated Learning[J].自动化学报（英文版）,2026,13(8):1938-1951,14.

基金项目

This work was supported in part by the National Natural Science Foundation of China(62576152,62332008,62336004),the Basic Research Program of Jiangsu(BK20250104),the Fundamental Research Funds for the Central Universities(JUSRP202504007),and the Leverhulme Trust Emeritus Fellowship(EM-2025-06-09). （62576152,62332008,62336004）

自动化学报（英文版）

ISSN：2329-9266

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