FedAvg算法就是在clients端进行多轮训练,然后server端对各个clients端的 w根据数据量占比进行聚合。算法流程如下: FedProx FedProx对clients端的Loss加了修正项,使得模型效果更好收敛更快: 其中clients端的Loss为: 所以每轮下降的梯度为: SCAFFOLD FedProx 与 SCAFFOLD都是用了一个全局模型去修正本地训练方向。 实验结...
Two new insights introduced by considering the statistical aspect are: (1) requiring the standard bounded dissimilarity is pessimistic for the convergence analysis of FedAvg and FedProx; (2) despite inconsistency of stationary points, their limiting points are unbiased estimators of the underlying truth...
TL;DR: Previous federated optization algorithms (such as FedAvg and FedProx) converge to stationary points of a mismatched objective function due to heterogeneity in data distribution. In this paper, the authors propose a data-sharing strategy to improve training on non-IID data by creating a sma...
def client_train_fedavg(data_ref, model, config, calculator): if config['parallel_type'] != 'obj': data = fus.sharable2dataset(data_ref) else: data = data_ref device = calculator.device data_loader = tud.DataLoader(data, batch_size=config['batch_size'], shuffle=True) optimizer = cal...
python main.py --FL fedavg --train_bs 50 --train_ep 5 --epoch 500 --non_alpha 0.5 --model lenet --dataset cifar_LDA --num_selected 10 --num_clients 100 About Federated Learning Algorithm (Pytorch) : FedAvg, FedProx, MOON, SCAFFOLD, FedDyn Resources Readme Activity Stars 18 ...
It subsumes previously proposed methods such as FedAvg and FedProx and provides the first principled understanding of the solution bias and the convergence slowdown due to objective inconsistency. Using insights from this analysis, we propose FedNova, a normalized averaging method that eliminates ...