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方法可以大致分为基于数据和基于模型的方法。 问题 现有的PFL方法要求大多数或所有参与
个性化特征空间：pFedFSL通过为每个客户学习个性化、有区别的特征空间，来适应不同客户的数据异质性。在新特征空间内，同一类别的样本距离更近，而不同类别的样本距离更远，这有助于提高模型的分类准确性和泛化能力。促进客户合作：pFedFSL鼓励具有相似数据分布的客户进行更多合作，通过确定模型参数在客户上的...
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...
Personalized federated learning (PFL), as a novel federated learning (FL) paradigm, is capable of generating personalized models for heterogenous clients. Combined with a meta-learning mechanism, PFL can further improve the convergence performance with few-shot training. However, meta-learning based ...
1 先瞄Title -- “PERSONALIZED FEDERATED LEARNING WITH FEATURE ALIGNMENT AND CLASSIFIER COLLABORATION” 1.1 Personalized FL 意味着客户端与服务端的模型参数会有所不同。 1.2 Feature Alignment 特征对齐这个词本身有点抽象,在不同子领域里面各有不同的理解。 1.3 Classifier Collaboration 分类器合作,意味着作者...
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources. In addition to typical limitations such as data, computation, and communication costs, access to the models is also often
This modification aims to enhance the encoding ability for feature extraction of federated learning, which helps to solve the imbalanced data problem by providing the feature characteristic for the few sample residences. As for the Decoder part, our goal is for it to better adapt to the power ...
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 ...
对于一些(generic FL)算法,在训练后往往会把局部模型丢弃,所以当我们 在personalized setting (P-FL)下评估时通常使用全局模型来评估,然而作者发现如果不把局部模型丢弃,在P-FL下评估它们会发现比所有已有的 P-FL algorithms还要好。真正让我们惊讶的是,即使没有大多数P-FL算法强加的显式正则化条件,G-FL算法的局...
Personalized Cross-Silo Federated Learning on Non-IID Data 在本文中,我们探索了一个新的想法,即促进具有类似数据的客户之间的成对协作。我们提出了FedAMP,一个采用联合周到的消息传递的新方法,以促进类似的客户进行更多的协作。我们建立了FedAMP对于凸和非凸模型的收敛性,并提出了一个启发式方法,以进一步提高Fed...