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CLC number: TP18

On-line Access: 2024-06-04

Received: 2023-04-23

Revision Accepted: 2024-06-04

Crosschecked: 2023-08-22

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhaohui WANG

https://orcid.org/0009-0007-3676-209X

Hongjiao LI

https://orcid.org/0000-0003-0642-9046

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.5 P.728-741

http://doi.org/10.1631/FITEE.2300284


Federated learning on non-IID and long-tailed data via dual-decoupling


Author(s):  Zhaohui WANG, Hongjiao LI, Jinguo LI, Renhao HU, Baojin WANG

Affiliation(s):  College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201306, China

Corresponding email(s):   hjli@shiep.edu.cn, lijg@shiep.edu.cn

Key Words:  Federated learning, Non-IID, Long-tailed data, Decoupling learning, Knowledge distillation


Zhaohui WANG, Hongjiao LI, Jinguo LI, Renhao HU, Baojin WANG. Federated learning on non-IID and long-tailed data via dual-decoupling[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 728-741.

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Abstract: 
federated learning (FL), a cutting-edge distributed machine learning training paradigm, aims to generate a global model by collaborating on the training of client models without revealing local private data. The co-occurrence of non-independent and identically distributed (non-IID) and long-tailed distribution in FL is one challenge that substantially degrades aggregate performance. In this paper, we present a corresponding solution called federated dual-decoupling via model and logit calibration (FedDDC) for non-IID and long-tailed distributions. The model is characterized by three aspects. First, we decouple the global model into the feature extractor and the classifier to fine-tune the components affected by the joint problem. For the biased feature extractor, we propose a client confidence re-weighting scheme to assist calibration, which assigns optimal weights to each client. For the biased classifier, we apply the classifier re-balancing method for fine-tuning. Then, we calibrate and integrate the client confidence re-weighted logits with the re-balanced logits to obtain the unbiased logits. Finally, we use decoupled knowledge distillation for the first time in the joint problem to enhance the accuracy of the global model by extracting the knowledge of the unbiased model. Numerous experiments demonstrate that on non-IID and long-tailed data in FL, our approach outperforms state-of-the-art methods.

基于非独立同分布和长尾数据的双解耦联邦学习

王朝晖,李红娇,李晋国,胡仁豪,王宝金
上海电力大学计算机科学与技术学院,中国上海市,201306
摘要:联邦学习(FL)作为一种最前沿的分布式机器学习训练范式,旨在通过协作训练客户端模型生成全局模型,且不泄露本地私有数据。然而客户端数据同时呈现出非独立同分布(non-IID)和长尾分布时会严重影响全局模型准确率,从而对联邦学习造成根本性挑战。针对非独立同分布和长尾数据,提出一种通过模型和逻辑校准的双解耦联邦学习(FedDDC)框架。该模型具有3个特点。首先,解耦全局模型为特征提取器和分类器,从而微调受异构数据影响的组件。针对有偏特征提取器,提出客户端置信度重加权算法辅助校准,该算法为每个客户端分配最优权重。针对有偏分类器,采用分类器再平衡方法进行微调。其次,校准并集成经过客户端重加权和分类器重平衡的逻辑,从而得到无偏逻辑。最后,首次在非独立同分布和长尾分布下的联邦学习中使用解耦知识蒸馏,通过提取无偏模型知识提高全局模型准确率。大量实验表明,在非独立同分布和长尾数据上FedDDC优于最先进的联邦学习算法。

关键词:联邦学习;非独立同分布;长尾数据;解耦学习;知识蒸馏

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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