On the convergence of fedavg on non-iid

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August undefined, 2024

Web14 de dez. de 2024 · The resulting model is then redistributed to clients for further training. To date, the most popular federated learning algorithm uses coordinate-wise averaging of the model parameters for aggregation (FedAvg). In this paper, we carry out a general mathematical convergence analysis to evaluate aggregation strategies in a FL framework. WebZhao, Yue, et al. "Federated learning with non-iid data." arXiv preprint arXiv:1806.00582 (2024). Sattler, Felix, et al. "Robust and communication-efficient federated learning from non-iid data." IEEE transactions on neural networks and learning systems (2024). Li, Xiang, et al. "On the convergence of fedavg on non-iid data."

Node Selection Toward Faster Convergence for Federated Learning on Non ...

Web20 de jul. de 2024 · For example, Li et al. analyzed the convergence of FedAvg algorithm on non-IID data and establish a convergence rate for strongly convex and smooth problems. Karimireddy et al. proposed tighter convergence rates for FedAvg algorithm for convex and non-convex functions with client sampling and heterogeneous data. Some … Web18 de fev. de 2024 · Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data … trony abbiategrasso

Benchmarking FedAvg and FedCurv for Image Classification Tasks

WebIn 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 and … WebIn this setting, local models might be strayed far from the local optimum of the complete dataset, thus possibly hindering the convergence of the federated model. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv), aiming at tackling the non-IID setting, have already been proposed. Web在这篇blog中我们一起来阅读一下 On the convergence of FedAvg on non-iid data 这篇 ICLR 2024 的paper. 主要目的. 本文的主要目的是证明联邦学习算法的收敛性。与之前其他工作 … trony alessandria

Node Selection Toward Faster Convergence for Federated …

WebCollaborative Fairness in Federated Learning. Hierarchically Fair Federated Learning. Incentive design for efficient federated learning in mobile networks: A contract theory … Web17 de dez. de 2024 · As for local training datasets, in order to control the degree of non-IID, we follow the classic method applied in ensemble-FedAvg . Taking MNIST as an example, we assign the sample with label i from the remained training dataset to the i -th group with probability $\varpi $ or to each remaining group with probability $\frac{1 - \varpi }{9} $ … trony airpodsWeb17 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 trony airtag

"WebOn the Convergence of FedAvg on Non-IID Data Xiang Li School of Mathematical Sciences Peking University Beijing, 100871, China [email protected] Kaixuan … " - On the convergence of fedavg on non-iid

On the convergence of fedavg on non-iid

Node Selection Toward Faster Convergence for Federated …

Web这不仅给算法设计带来了挑战，也使得理论分析更加困难。虽然FedAvg在数据为非iid时确实有效[20]，但即使在凸优化设置中，非iid数据上的FedAvg也缺乏理论保证。在假设(1) … Web1 de jan. de 2024 · However, due to lack of theoretical basis for Non-IID data, in order to provide insight for a conceptual understanding of FedAvg, Li et al. formulated strongly convex and smooth problems, establish a convergence rate $\mathcal {O}(\frac{1}{T})$ by analyzing the convergence of FedAvg .

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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 … WebOn the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189. About. FedAVG with Dirichlet distribution MNIST datasets Resources. Readme Stars. 4 stars Watchers. 1 watching Forks. 1 fork Report repository Releases No releases published. Packages 0. No packages published . Languages. Python 100.0%;

Web7 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 … WebDespite 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 ...

WebFedAvg 是经典高效的 FL 算法，但是在现实环境下缺乏理论保障。本文分析了 FedAvg 在 Non-IID 数据上的收敛性，得到了强凸光滑条件下的收敛率 \mathcal {O} (\frac {1} {T}) ， … WebOn the Convergence of FedAvg on Non-IID Data. X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang. ICLR , OpenReview.net ... search on. Google Scholar Microsoft Bing WorldCat BASE. Tags convergence dblp iclr2024 optimization. Users. Comments and Reviews. This publication has not been reviewed yet. rating distribution. average user rating 0.0 out of ...

WebOn the Convergence of FedAvg on Non-IID Data. 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 FedAvg and how different sampling and averaging schemes affect its convergence.. Our code is based on the codes for FedProx, another …

Web"On the convergence of fedavg on non-iid data." arXiv preprint arXiv:1907.02189 (2024). Special Topic 3: Model Compression. Cheng, Yu, et al. "A survey of model compression … trony antivirusWebFederated learning (FL) is a machine learning paradigm where a shared central model is learned across distributed devices while the training data remains on these devices. Federated Averaging (FedAvg) is the leading optimization method for training non-convex models in this setting with a synchronized protocol. However, the assumptions made by … trony airpods proWebOn 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 … trony apertoWeb11 de abr. de 2024 · 实验表明在non-IID的数据上，联邦学习模型的表现非常差; 挑战高度异构数据的收敛性差：当对non-iid数据进行学习时，FedAvg的准确性显著降低。这种性能下降归因于客户端漂移的现象，这是由于对non-iid的本地数据分布进行了一轮又一轮的本地训练和同步的结果。 trony aperturaWeb20 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 ... trony ancona offerte volantinoWeb7 de out. de 2024 · Non i.i.d. data is shown to impact both the convergence speed and the final performance of the FedAvg algorithm [13, 21]. [ 13 , 30 ] tackle data heterogeneity by sharing a limited common dataset. IDA [ 28 ] proposes to stabilize and improve the learning process by weighting the clients’ updates based on their distance from the global model. trony albino orariWebX. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang. On the convergence of fedavg on non-iid data. In Proceedings of the 8th International Conference on Learning Representations (ICLR), 2024. Google Scholar; H Brendan McMahan and et al. Communication-efficient learning of deep networks from decentralized data. trony alexa