一. 前言 《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,...
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 论文笔记 /104632718 论文通过实验验证了,在non-IID数据中,使用FedAvg算法训练的模型会使准确率降低。 从图中可以看出在non-IID使用FedAvg算法训练的模型准确率有了明显的下降,但是对于IID...因为数据分布的不同。FedAvg算法训练的模型准确率收到数据分布偏态性的影响。 研究方法...
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 ...
论文笔记:arXiv'22 Knowledge-Aware Federated Active Learning with Non-IID Data Abstract 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地数据的一个障碍。在本文中,我们提出了一种联合主动学习… ...
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。
A survey on federated learning Authors Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, Yuan Gao Keywords Federated learning; Privacy protection; Mac
device 上面的数据是 IID （独立同分布）的，那么 Federated Learning 就相当于是 Distributed Learning。
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...
Robustness of federated learning has become one of the major concerns since some Byzantine adversaries, who may upload false data owning to unreliable communication channels, corrupted hardware or even malicious attacks, might be concealed in the group of the distributed worker. Meanwhile, it has been...