《No fear of heterogeneity: Classifier calibration for federated learning with non-iid data》发表在 2021 年的机器学习顶级会议 NeurIPS 上 [1],此篇论文旨在解决联邦学习客户端数据分布异构的问题,并设计了一个简洁且优美的分类器矫正方法。此篇论文的叙事非常巧妙,通过实验观察一步一步引出创新点,并且所提方法...
Knowledge-Aware Federated Active Learning with Non-IID DataAbstract联合学习使多个分散的客户端能够在不共享本地训练数据的情况下进行协作学习。然而,在本地客户端上获取数据标签的昂贵注释成本仍然是利用本…
Federated Learning with Non-IID Data 论文中分析了FedAvg算法在Non-IID数据时,准确率下降的原因。并提出共享5%的数据可提高准确率。 Federated Learning with Non-IID Data Abstract 联邦学习支持边缘受限的计算设备(例如移动电话和物联网设备)在保持训练本地数据的同时共享模型。用去中心化的方法训练模型保障了隐私 ...
L−smooth is the only required condition and their analysis is based on that solvingWkis feasible. As discussed inmy previous bolg, we should proveWkis convergent toW∗along withk→∞. According to the definition of strongly convex, we have ...
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data 论文阅读笔记,程序员大本营,技术文章内容聚合第一站。
Keywords Federated learning; Privacy protection; Machine learning Abstract 联邦学习是在一个中央聚合器的协调下多客户协作解决机器学习问题的机制,它允许数据分散训练以确保每个设备的数据隐私。联邦学习基于两个主要思想:本地计算、模型传输,减少了一些由传统的集中式机器学习方法带来的系统性的隐私风险和代价。客户端的...
Harry Hsu et al. [76] proposed aserver-side momentum implementation(服务端动量,会大大地降低恶意客户端的影响) specifically for the use of non-IID client datasets. Combined with the random client selection native to federated learning [2] this may greatly reduce the influence a malicious client ha...
Federated Learning is a framework to train a centralized model for a task where the data is de-centralized across different devices/ silos. This helps preserve privacy of data on various devices as only the weight updates are shared with the centralized model so the data can remain on each de...
本文在去中心化联邦学习场景下,解决Non-IID数据造成的模型性能不好的问题。(We tackle the non-convex problem of learning a personalized deep learning model in a decentralized setting.) 1、背景 联邦学习分为中心化和去中心化两种架构,在中心化联邦学习架构中,中心服务器就如上帝之手,控制系统的整个学习过程...
Given the mismatch problem in federated active learning, informative data on each client may not be that informative to the global model due to the non-IID data distributions, i.e., it is not reliable to use only one of them for active sampling. Computing the model discrepancy between each...