当以“federated personal”为关键词,在dblp上进行文献检索的时候,会发现个性化联邦学习(PFL, Personalized Federated Learning)的研究在2021年的有明显增加。 经小鱼的查询,至22年4月16日针对个性化联邦学习的综述文章仅两篇[1][2],其中写得比较全面的一篇已被IEEE Transactions on Neural Networks and Learning Syste...
当以“federated personal”为关键词,在 dblp 上进行文献检索的时候,会发现个性化联邦学习(PFL, Personalized Federated Learning)的研究在2021年的有明显增加。 经小鱼的查询,至22年4月16日针对个性化联邦学习的综述文章仅两篇[1][2],其中...
先是一个 survey: Towards Personalized Federated Learning 可以分成 data-based,model-based;其中 model-based 按照 FL 阶段会产生多少个不同的 model,有可以分成 single-model,multi-model,和 n-model 下面根据上图,对一些分支的文献进行一个整理: Data-based FedPer_Federated Learning with Non-IID Data:Server...
原文的分类标准是根据(1)解决数据异质性和(2)达成模型个性化这两个需求进行划分的。但是或许这种划分并不是绝对的:Learning Personalized Models也可也解决数据异质性,反之亦然。 Survey of personalization techniques for federated learning 一文提供了另一种分类方案,也就是从使用的技术层面对个性化联邦学习进行分类。
Traditional federated learning has shown mediocre performance on heterogeneous data, thus sparking increasing interest in personalized federated learning. Unlike traditional federated learning, which trains a single global consensual model, personalized federated learning allows for the provision of distinct model...
Survey on user location prediction based on geo-social networking data Article 31 January 2020 Spatio-Temporal Aware Next Point-of-Interest Recommendation with Privacy Preserving Chapter © 2024 Deep Learning-based Privacy-preserving Publishing Method for Location Big Data in Vehicular Networks ...
A survey on federated learning for resource-constrained IoT devices IEEE Internet Things J. (2021) NguyenD. et al. Federated learning for smart healthcare: A survey ACM Comput. Surv. (2022) C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, Y. Gao, A survey on federated learning, Knowl....
Personalized federated learning aims to address data heterogeneity across local clients in federated learning. However, current methods blindly incorporate either full model parameters or predefined partial parameters in personalized federated learning. They fail to customize the collaboration manner according to...
federated learning-based finger vein authentication framework (FedFV) to solve the problem of small sample size and category diversity while protecting user privacy. Through training under FedFV, each client can share the knowledge learned from its user’s finger vein data with the federated client...
a self-adaptive multi-task learning framework to automatically tune hyperparameters more effective negative sampling more robust and accurate both offline and online learning techniques 11.5 Privacy-preserving News Recommendation federated learning techniques 11.6 Diversity-aware News Recommendation 11.7 Debiasing...