FedAvg: Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data - intworist/FedAvg

2025年2月17日 · 真诚求解答，为什么联邦学习文献里的fedavg准确率差别很大？ 比如这篇发布在NeurIPS 2022中的Preservation of the Global Knowledge by Not-True Disti… 显示全部

Simple implementation of FedAvg, a Federated Learning algorithm. - naderAsadi/FedAvg

当你看懂、并成功运行fedavg后，你对联邦学习的理解就会进入第二阶段 在此之前，如果你没学过pytorch，可以找个短一些的视频，10个小时就足够啦！ 真的不建议，上来就看30+小时的合集，没必要！

FedAvg-Algorithm This repository contains a PyTorch implementation of the Federated Averaging (FedAvg) algorithm for federated learning. FedAvg is a popular federated learning technique used for training machine learning models on decentralized data sources while preserving data privacy.

FedAvg: 每个设备都在本地进行深度学习模型的训练（比如SGD)，而在训练的过程中，进行多次迭代(iteration=Data_Size / Batch_Size)。然后将训练好的模型发送至服务器，并聚合（加权平均）。 注意：一般FedAvg中迭代次数（iteration）和轮次(Epoch)是不同的概念。

联邦学习FedAvg算法在每一次采样用户后，将等待所有当前被采样的用户发回其本地训练的模型后，才进行一步全局模型更新，这种方式被称作同步（Synchronous）更新。

FederatedAverage - an implementation of Federated Learning. The Federated Averaging (FedAvg) algorithm, which consists of alternating between a few local stochastic gradient updates at client nodes, followed by a model averaging update at the server, is perhaps the most commonly used method in Federated Learning - kondster/FedAvg

FedAvg and FedSGD (McMahan et al., 2016) Communication-Efficient Learning of Deep Networks from Decentralized Data; FedAvgM (Hsu et al., 2019) Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification; FedProx (Li et al., 2018) Federated Optimization in Heterogeneous Networks

Flower: A Friendly Federated AI Framework. Contribute to adap/flower development by creating an account on GitHub.