二、Distributed learning vs. Federated Learning 三、Communication efficiency 四、Privacy 五、Adversarial Robustness [Parallel Computing for Machine Learning(一) - 知乎 (zhihu.com)] [Parallel Computing for Machine Learning(二) - 知乎 (zhihu.com)] [Parallel Computing for Machine Learning(三) - 知乎 ...
那么 Federated Learning 就相当于是 Distributed Learning。
分布式学习(Distributed Learning) 该方案中,聚合器收集设备本地训练的模型,以提供对所研究参数的整体和更准确的估计,但设备本地并不需要通过聚合器的任何反馈来获取全局模型。 并行学习(Parallel Learning) 并行学习的主要目标是拓展算法规模、加速学习过程,或者二者兼而有之。在这种类型的学习中,中央参数服务器上的可...
因此，联邦学习与这种联合学习是完全不同的概念。还有一种学习方法叫做“多任务学习”(multitask learning)[8,9]，它是迁移学习的一个子方向，旨在有多个学习目标并部分共用数据的情况下，尽量多地利用共有模型部分来提高学习效果。多任务学习对数据安全和隐私也没有提出要求，而是一种机器学习算法。联邦学习概述 什么...
federated learning vs. distributed machine learning federated learning vs. edge computing federated learning vs. federated database systems Applications Federated learning and data alliance of enterprises Conclusions and prospects Introduction 当今的AI仍然面临两个主要挑战。 一是在大多数行业中,数据以孤立的孤岛...
How does federated learning differ from traditional distributed learning? 1. Users have control over their device and data 用户可以控制数据和设备 2. Worker nodes are unstable 手机node不稳定 3. Communication cost is higher than computation cost. ...
The computing node can then train a second ML model on the subset, where the training of the second ML model is performed using the distributed/federated training approach.YANIV BEN-ITZHAKSHAY VARGAFTIK
In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of laws or r
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond Authors Sawsan AbdulRahman, Hanine Tout, Hakim
Motivated by broad applications in reinforcement learning and federated learning, we study local stochastic approximation over a network of agents, where their goal is to find the root of an operator composed of the local operators at the agents. Our focus is to characterize the finite-time perfor...