arXiv:1610.05492 - Federated Learning: Strategies for Improving Communication Efficiency Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon [paper] 1. Main Idea 这篇论文所研究的是联邦学习的通信效率问题。论文研究的场景限定于同步联邦学习算法(sy...
(阅读笔记)Federated Learning: Strategies for Improving Communication Efficiency,程序员大本营,技术文章内容聚合第一站。
[1]Federated Learning: Collaborative Machine Learning without Centralized Training Data, 2017.https://ai.googleblog.com/2017/04/federated-learning-collaborative.html. [2]Federated Learning.https://federated.withgoogle.com. [3] Federated Learning: Strategies for Improving Communication Efficiency, 2016.ht...
联邦学习(federated learning)将模型训练任务部署在移动边缘设备,参与者只需将训练后的本地模型发送到服务器参与全局聚合而无须发送原始数据,提高了数据隐私性.然而, 解决效率问题是联邦学… 专知 AAAI2022 | 联邦学习专题—Federated Learning for Face Recognition with Gradient Correction AAAI2022第二篇联邦学习论文。
Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw
(4) Federated Learning: Strategies for Improving Communication Efficiency, https://arxiv.org/abs/1610.05492 (5) Federated Optimization: Distributed Machine Learning for On-Device Intelligence,https://arxiv.org/abs/1610.02527
I hope this activates your interest in federated learning and its potential to drive a new wave of on-device training, application personalization, and increased data privacy. If so, check out the resources below to learn more: Federated Learning: Strategies for Improving Communication Efficiency ...
Konečný, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. CoRR (2016) arxiv:1610.05492 McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient le...
[5] Konečný J, McMahan H B, Yu F X, et al. Federated learning: Strategies forimproving communication efficiency[J]. arXiv preprint arXiv:1610.05492, 2016. [6] McMahan H B, Moore E, Ramage D, et al. Communication efficient learning of deep networks from decentralized data[J]. ar...
(4) Federated Learning: Strategies for Improving Communication Efficiency,https://arxiv.org/abs/1610.05492 (5) Federated Optimization: Distributed Machine Learning for On-Device Intelligence,https://arxiv.org/abs/1610.02527 欢迎关注微信公众号【数说工作室】,微信ID:shushuojun...