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的客户有足够的...
[1]Cai, Dongqi, et al. "Federated few-shot learning for mobile NLP." Proceedings of the 29th Annual International Conference on Mobile Computing and Networking. 2023. 我是 @没饭了 ,想变得有趣的一个顶无趣的家伙。感谢您对本文的阅读,求赞求收藏求关注,您的反馈是我创作的最大动力! 编辑于 2024...
个性化联邦小样本学习是一种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的方法。其主要特点和优势如下：解决小样本挑战：个性化联邦学习传统方法通常要求客户有足够的训练样本以诱导个性化模型，但在小样本情况下效果不佳。同时，小样本学习方法通常需要集中训练数据，不适用于数据分散的场景。pFedFSL专...
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...
Shome and Tejaswini [136] suggested a research that focused on the generalization problem to deploy a system in a real-world environment. They [136] proposed few-shot federated learning for FER (FedAffect), thus tackling the problem of generalization on unseen data. FedAffect is a novel feder...
联邦学习(Federated Learning,FL)是一种利用分散数据训练机器学习(ML)模型的流行技术。已有大量著作对 FL 最终训练得到的全局模型的性能进行了研究,但仍不清楚训练过程如何影响最终测试的准确性。 联邦结点分类与可伸缩图Transformer 图被广泛用于模型关系数据。随着图在现实世界中越来越大,有一种趋势是在多个局部系统中...
Few-shot model agnostic federated learning is a technique used in machine learning that allows multiple parties with different data sets to train a shared model without sharing the data. In traditional federated learning, each party would contribute their data to a central server where the model is...
we propose a few-shot federated learning framework which utilizes few samples of labeled data to train local models in each training round and aggregates all the local model weights in the central server to get a globally optimal model. for use of large scale of unlabeled data, we also desig...
(annotated) data used in multiple tasks. To mitigate this issue, we propose FewFedWeight, a few-shot federated learning framework across multiple tasks, to achieve the best of both worlds: privacy preservation and cross-task generalization. FewFedWeight trains client models in isolated devices ...
this work investigates federated NLP in the few-shot scenario (FedFSL). By retrofitting algorithmic advances of pseudo labeling and prompt learning, we first establish a training pipeline that delivers competitive accuracy when only 0.05% (fewer than 100) of the training data is labeled and the ...