笼统的讲，与其说 Federated Learning 是做 Decentralized optimization 的机器学习，倒不如说是用 Distribu...
期初，联邦学习（Federated Learning）被谷歌提出时，是运用在横向联邦学习中，就是我们常见的对谷歌键盘...
2 The FederatedAveraging Algorithm 3 Experimental Results 总结 写在前面 大家好!我是入门科研没多久的小白,从今天开始,我决定对阅读的每篇论文做阅读笔记,以提高自己的论文阅读效果。 关于联邦学习,我的阅读笔记将联邦学习开山之作《Communication-Efficient Learning of Deep Networks from Decentralized Data》开始。这...
In this paper, we propose a novel framework of federated learning equipped with the process of decentralized knowledge distillation (FedDKD) (i.e., without data on the server). The FedDKD introduces a module of decentralized knowledge distillation (DKD) to distill the knowledge of the local ...
Machine learning over distributed data stored by many clients has important applications in use cases where data privacy is a key concern or central data storage is not an option. Recently, federated learning was proposed to solve this problem. The assumption is that the data itself is not ...
The performance of federated learning in neural networks is generally influenced by the heterogeneity of the data distribution. For a well-performing global model, taking a weighted average of the local models, as done by most existing federated learning algorithms, may not guarantee consistency with...
This diagnostic study investigates the performance of a privacy-preserving federated learning approach vs a classical centralized and ensemble learning
Decentralized federated learning (DFL) is a powerful framework of distributed machine learning and decentralized stochastic gradient descent (SGD) is a driving engine for DFL. The performance of decentralized SGD is jointly influenced by communication-efficiency and convergence rate. In this paper, we ...
以下是一些与 decentralized federated learning 相关的代码库和项目: 1. **FedML**: FedML 是一个开源的机器学习框架,它支持联邦学习、分布式机器学习和边缘计算。它提供了一个易于使用的 API,以便开发人员可以轻松地实现自己的联邦学习算法。FedML 支持多种联邦学习算法,包括加权平均、深度平均网络和联邦迁移学习。
2.1 Federated Learning 联邦学习算法的伪代码见算法1(主)和算法2(worker)。 2.2 Gossip Learning 八卦学习是一种从完全分布式数据中学习模型的方法。 3 Algorithms 算法1采用了方法aggregate。它的功能是解压缩和聚合用压缩编码的接收梯度。当没有实际的压缩(算法7中compressNone)时,只取梯度的平均值(聚合算法6中的...