Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra.Federated Learning with Non-IID Data arXiv:1806.00582. Paper TL;DR: Previous federated optization algorithms (such as FedAvg and FedProx)
An implementation for "Federated Learning with Non-IID Data via Local Drift Decoupling and Correction" - gaoliang13/FedDC
In Differentially Private Federated Learning (DPFL), gradient clipping and random noise addition disproportionately affect statistically heterogeneous data. As a consequence, DPFL has a disparate impact: the accuracy of models trained with DPFL tends to decrease more on these data. If the accuracy ...
GitHub - Xtra-Computing/NIID-Bench: Federated Learning on Non-IID Data Silos: An Experimental Study (ICDE 2022)github.com/Xtra-Computing/NIID-Bench 背景介绍: 简单说,每一方利用本地数据训练一个结构相同的模型,将权重信息送至中心服务器进行聚合。将聚合后的权重返还至参与方手中,完成一轮的训练。
key point:针对 feature distribution skew(属于 Non-IID 数据分布的一种情况,后面会详细介绍),在客户端上传模型参数之前在本地执行 batch-norm layers。 2.6 Addressing Class Imbalance in Federated Learning key point:增加一个 monitor 来监控 imbalance class distribution 并基于此提出新的损失函数。 2.7 MOON: ...
The poorest model generalization performances are shown on institutions 2, 3 and 6, which have the smallest data contributions of the group. Collaborative learning is superior We evaluate the benefits of collaborative learning with respect to improving both scores on an institution’s own data, and...
,wenkehuang,yemang}@whu.edu.cn https://github.com/xiyuanyang45/FedAS Abstract Personalized Federated Learning (PFL) is primarily de- signed to provide customized models for each client to bet- ter fit the non-iid distributed client data, which is a inherent challenge i...
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra.Federated Learning with Non-IID Data arXiv:1806.00582. Paper TL;DR: Previous federated optization algorithms (such as FedAvg and FedProx) converge to stationary points of a mismatched objective function due to heterogeneity...
FedConcat: Exploiting Label Skew in Federated Learning with Model Concatenation (AAAI 2024) We publish NIID-Bench challengehttps://niidbench.xtra.science, a benchmark to compare federated learning algorithms on comprehensive non-IID data settings. Researchers are welcome to test their algorithms on th...
Classical federated learning approaches incur significant performance degradation in the presence of non-IID client data. A possible direction to address this issue is forming clusters of clients with roughly IID data. Most solutions following this direction are iterative and relatively slow, also prone...