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 ...
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 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...
Federated learning on non-IID data: A survey. Neurocomputing 2021, 465, 371–390. [Google Scholar] [CrossRef] Zhang, Y.; Chen, Y.; Wang, Y.; Liu, Q.; Cheng, A. CSI-based human activity recognition with graph few-shot learning. IEEE Internet Things J. 2022, 9, 4139–4151. [...
Additionally, the scheme adopts the one-shot federated learning paradigm, where each client uploads their local model containing private information only once throughout the training process. This approach not only reduces the risk of privacy leakage but also decreases the communication overhead 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 ...