On the convergence of fedavg on non-iid
Web24 de nov. de 2024 · This repository contains the codes for the paper. On the Convergence of FedAvg on Non-IID Data. Our paper is a tentative theoretical understanding towards … Web17 de mar. de 2024 · On the convergence of fedavg on non-iid data. In International Conference on Learning Representations, 2024. 1 Ensemble distillation for robust model fusion in federated learning
On the convergence of fedavg on non-iid
Did you know?
WebXiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189, 2024. Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi. Ensemble distillation for robust model fusion in federated learning. Advances in Neural Information Processing Systems, … WebAveraging (FedAvg) runs Stochastic Gradient Descent (SGD) in parallel on a small subset of the total devices and averages the sequences only once in a while. Despite its simplicity, it lacks theoretical guarantees under realistic settings. In this paper, we analyze the convergence of FedAvg on non-iid data and establish a convergence rate of O(1 T
Web论文阅读 Federated Machine Learning: Concept and Applications 联邦学习的实现架构 A Communication-Efficient Collaborative Learning Framework for Distributed Features CatBoost: unbiased boosting with categorical features Advances and Open Problems in Federated Learning Relaxing the Core FL Assumptions: Applications to Emerging … Web12 de out. de 2024 · FedAvg is a FL algorithm which has been the subject of much study, however it suffers from a large number of rounds to convergence with non-Independent, Identically Distributed (non-IID) client ...
WebOn the Convergence of FedAvg on Non-IID Data - YouTube 0:00 / 13:58 On the Convergence of FedAvg on Non-IID Data 206 views Mar 16, 2024 5 Dislike Share Save … Web20 de nov. de 2024 · In general, pFedMe outperforms FedAvg on the convergence rate, but there are too many hyperparameters need to be ... Experimental results have shown that FedPer can achieve much higher test accuracy than FedAvg, especially on strongly Non-IID data. And it is surprising to find that FedPer has achieved better performance on Non-IID ...
Webprovided new convergence analysis of the well-known federated average (FedAvg) in the non-independent and identically distributed (non-IID) data setting and partial clients …
WebOn the Convergence of FedAvg on Non-IID Data Xiang Li School of Mathematical Sciences Peking University Beijing, 100871, China [email protected] Kaixuan Huang School of Mathematical Sciences Peking University Beijing, 100871, China [email protected] Wenhao Yang Center for Data Science Peking University … ts 轉 mp4 線上Web23 de mai. de 2024 · Federated learning (FL) can tackle the problem of data silos of asymmetric information and privacy leakage; however, it still has shortcomings, such as … ts 遍历interface属性Web10 de out. de 2024 · On the convergence of fedavg on non-iid data[J]. arXiv preprint arXiv:1907.02189, 2024. [3] Wang H, Kaplan Z, Niu D, et al. Optimizing Federated … ts 重写 interfaceWeb4 de jul. de 2024 · In this paper, we analyze the convergence of \texttt{FedAvg} on non-iid data and establish a convergence rate of $\mathcal{O}(\frac{1}{T})$ for strongly convex … ts里面的thisWeb7 de mai. de 2024 · It dynamically accelerates convergence on non-IID data and resists performance deterioration caused by the staleness effect simultaneously using a two-phase training mechanism. Theoretical analysis and experimental results prove that our approach converges faster with fewer communication rounds than baselines and can resist the … ts 遍历objectWeb14 de abr. de 2024 · For Non-IID data, the accuracy of MChain-SFFL is better than other comparison methods, and MChain-SFFL can effectively improve the convergence speed of the model. For IID data, the accuracy and convergence speed of MChain-SFFL are close to Chain-PPFL and FedAVG. ts 遍历interfaceWebDespite its simplicity, it lacks theoretical guarantees in the federated setting. In this paper, we analyze the convergence of \texttt {FedAvg} on non-iid data. We investigate the effect of different sampling and averaging schemes, which are crucial especially when data are unbalanced. We prove a concise convergence rate of $\mathcal {O} (\frac ... ts里的type