The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy ...
The EdgeFL Framework simplifies the integration of decentralized federated learning into your machine learning applications. - HarryME-zh/EdgeFL
AddRemoveMark official No code implementations yet. Submityour code now Federated Learning Datasets Edit CelebA Submitresults from this paperto get state-of-the-art GitHub badges and help the community compare results to other papers. Methods ...
原文链接: http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf 发表:PMLR2017 ——联邦学习的开山之作 code: https://github.com/shaoxiongji/federated-learning 编辑: 古月 提出联邦学习的概念: FL的定义:一种特殊的分布式学习方法 传统ML的优化目标 在一般的机器学习或者深度学习中,最终的目标都是最...
本地学习(Local Learning) 中心化学习(Central Learning) 联邦学习(Federated Learning)3. 对比性分析相较于本地学习,结合各方数据,训练共同模型,性能要比每个节点本地训练效果要好 相较于中心化学习,数据不集中,保护数据隐私 相较于联邦学习 不需要中心协调服务器,提高弹性和容错性 联邦学习仅支持星型拓扑结构...
Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and chal- lenges. IEEE Communications Surveys & Tutorials, 2023. 1 [7] Albert S Berahas, Raghu Bollapragada, Nitish Shirish Keskar, and Ermin Wei. Balancing communicatio...
联邦学习可以通过将攻击限制在客户端上,而不是客户端和服务器上,从而大大降低隐私和安全风险。基于此为了实现联邦学习,本文提出 FederatedAveraging(FedAvg),将每个客户端的 local 随机梯度下降(SGD)与一个执行模型平均化的服务器端结合起来。 联邦学习中隐含的优化问题称为联邦优化,与分布式优化形成了联系(和对比)。
(centralized) federated learning (FL)23,24, in which multiple AI models are trained independently on separate computers (peers). In FL, peers do not share any input data with each other, and only share the learned model weights. However, a central coordinator governs the learning progress ...
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data - AshwinRJ/Federated-Learning-PyTorch
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data - HNG-94/Federated-Learning-PyTorch