这篇论文的名字是Adaptive Federated Learning,它的会议版本是发了18年的infocom,当时的名字叫WhenEdge Meets learning,有意思的是当时这篇文章里提的是Distributed Learning,而几乎没有提到FederatedLearning,只在实验部分的baseline里提到了,但在期刊版本里内容几乎没改动,但是把title改成了FederatedLearning,而且文章里所有...
client-variance-reduction (CVR) schemes被提出,但都有缺陷,比如有些没考虑异构局部更新,有些在non-iid下收敛还是慢。 最近的研究表明,基于交替方向乘子法( Alternating Direction Method of Multipliers,ADMM )的原始-对偶FL方法具有对数据和系统异质性的内在鲁棒性,如FedPD、Fed ADMM和FedDyn。 然而,分布式ADMM算法...
Federated learning (FL) aims to preserve data privacy through distributed learning methods that keep the data in storage silos. Likewise, differential privacy improves data privacy by measuring the privacy loss in communication among the elements of FL. The paper proposes two adaptiv...
Variations in sequence, scanner, and patient population cause domain shifts in MRI.Data with domain shift leads to poor generalization in Federated learning.Proposed an adaptive federated meta-learning model for classification.Sparse models exclude domain-dependent information and capture common traits.关键...
百度文库 其他 adaptive personalized federated learningadaptive personalized federated learning adaptive personalized federated learning翻译为:自适应个性化联邦学习。©2022 Baidu |由 百度智能云 提供计算服务 | 使用百度前必读 | 文库协议 | 网站地图 | 百度营销 ...
多任务学习(multi_task learning):对个性化问题的另一种观点是视为多任务学习问题。这种设置下对每个客户端的优化可以看做是一个新的任务。 情景化(Contextualization):个性化联邦学习中的一个重要应用是在不同情境下使用模型。我们需要在不同的环境下对一个客户端进行个性化的模型。
The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions. 展开 关键词: Distributed machine learning federated learning mobile edge computing wireless networking ...
IBM/adaptive-federated-learning master 2Branches0Tags Code Folders and files Name Last commit message Last commit date Latest commit shiqiangw Add BibTeX to Readme Apr 12, 2023 b6bc482·Apr 12, 2023 History 3 Commits control_algorithm Initial commit...
内容提示: 1Adaptive Federated Learning in ResourceConstrained Edge Computing SystemsShiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung,Christian Makaya, Ting He, Kevin ChanAbstract—Emerging technologies and applications includingInternet of Things (IoT), social networking, and crowd-sourcing...
文章的其他实验对损失值、准确率、不同边缘节点数目、不同时延约束的情况都做了讨论,简直不要太全面。 以后还会回来细品。 参考 ^Adaptive Federated Learning in Resource Constrained Edge Computing Systemshttps://ieeexplore.ieee.org/document/8664630