On the convergence of fedavg on non-iid
Web14 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 … 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 …
On the convergence of fedavg on non-iid
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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 … 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 sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between the global and local objectives, making the FL model slow to …
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 ... WebOn the Convergence of FedAvg on Non-IID Data Xiang Li School of Mathematical Sciences Peking University Beijing, 100871, China [email protected] Kaixuan …
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 … WebExperimental results demonstrate the effectiveness of FedPNS in accelerating the FL convergence rate, as compared to FedAvg with random node ... 登录/注册. Node …
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 …
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." fish lake nova scotiaWebDespite its simplicity, it lacks theoretical guarantees under realistic settings. In this paper, we analyze the convergence of exttt {FedAvg} on non-iid data and establish a … can chlorpheniramine be abusedWeb20 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 … can chlorsig be used on dogsWeb10 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 … can chlorpheniramine maleate make you tiredWebguarantees in the federated setting. In this paper, we analyze the convergence of FedAvg on non-iid data. We investigate the effect of different sampling and averaging schemes, … can chlortabs cause high blood pressureWeb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) … can chlorpheniramine get you highWeb3 de jul. de 2024 · 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 … can chlorsig ointment be used on wounds