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Poster
DENSE: Data-Free One-Shot Federated Learning
Jie Zhang · Chen Chen · Bo Li · Lingjuan Lyu · Shuang Wu · Shouhong Ding · Chunhua Shen · Chao Wu

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #139
in Poster Session 2 »

One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round. Despite the low communication cost, existing one-shot FL methods are mostly impractical or face inherent limitations, \eg a public dataset is required, clients' models are homogeneous, and additional data/model information need to be uploaded. To overcome these issues, we propose a novel two-stage \textbf{D}ata-fre\textbf{E} o\textbf{N}e-\textbf{S}hot federated l\textbf{E}arning (DENSE) framework, which trains the global model by a data generation stage and a model distillation stage. DENSE is a practical one-shot FL method that can be applied in reality due to the following advantages:(1) DENSE requires no additional information compared with other methods (except the model parameters) to be transferred between clients and the server;(2) DENSE does not require any auxiliary dataset for training;(3) DENSE considers model heterogeneity in FL, \ie different clients can have different model architectures.Experiments on a variety of real-world datasets demonstrate the superiority of our method.For example, DENSE outperforms the best baseline method Fed-ADI by 5.08\% on CIFAR10 dataset.

Keywords: federated learning · data-free knowledge distillation · one-shot FL
[ Paper]  [ Slides]  [ Poster]  [ OpenReview] 

Author Information

Jie Zhang (Zhejiang University)
Chen Chen (Zhejiang University)
Bo Li (Nanjing University)
Lingjuan Lyu (Sony AI)
Shuang Wu (Tencent YouTu Lab)
Shouhong Ding (Tencent Youtu Lab)
Chunhua Shen (University of Adelaide)
Chao Wu (Zhejiang University)

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