不同客户端的数据异质性使得整个局部数据难以与单一全局模型进行拟合,进而影响了模型的性能和收敛速度。因此,个性化联邦学习(Personalized Federated Learning, PFL) 被提出来解决上述问题。PFL的目标是为每个参与的客户共同学习一个个性化的模型,学习到的局部模型的目标是能够很好地拟合客户的不同局部数据。大多数现有的PFL...
个性化联邦小样本学习（pFedFSL）旨在解决在客户训练样本有限情况下的个性化联邦学习问题。现有PFL解决方案通常假设客户有足够的训练样本以共同诱导个性化模型，但在小样本情况下效果不佳。同时，传统小样本学习方法要求集中训练数据，不适用于分散场景。pFedFSL通过识别哪些模型在哪些客户上表现良好，为每个客户学...
2.1 FL中数据异构的特点 -- “Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. ” 2.2 常见解决异构的方式:对于不同的任务,采用公共的特征表示方式以及不同的分类头 -- “One su...
FedAS: Bridging Inconsistency in Personalized Federated Learning Xiyuan Yang1 Wenke Huang1 Mang Ye1,2* 1National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China 2Taikang Center for Life and Medical Sciences, Wuhan ...
Personalized Federated Learning using Hypernetworks 摘要 个性化联合学习的任务是为多个客户训练机器学习模型,每个客户有自己的数据分布。目标是协同训练个性化的模型,同时考虑到客户之间的数据差异并减少通信成本。我们提出了一种使用超网络解决这个问题的新方法,称为pFedHN,即个性化联合超网络。在这种方法中,一个中央超...
【ICLR 2022】On Bridging Generic and Personalized Federated Learning for Image Classification FL的标准设置寻求训练能够很好地处理通用数据分布的单一的“全局”模型(generic FL) FL的另一种设置试图通过为每个客户构建与客户的个性化数据捆绑在一起的“个性化”模型来承认客户之间的异质性(personalized FL)(为每个...
To address the cross-domain problem, we propose a personalized federated learning approach based on bidirectional knowledge distillation for WiFi gesture recognition (pFedBKD). First, during the local training process of the client, we use knowledge distillation (KD) to extract the knowledge of the ...
Moreover, federated learning is also a machine learning method that enables machine learning models to obtain experience from different datasets located at different sites (e.g., local data centers, a central server) without sharing the training data. This allows for personal data to remain in ...