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...
我们首先提供联合学习的纯沟通版本。 在t≥0的回合中,服务器将当前模型Wt分配给n t个客户端的子集St。 这些客户端根据其本地数据独立更新模型。 让更新的局部模型为Wt1 Wt2…,这样客户端i的更新可以写为 。 这些更新可能是在客户端上计算的单个梯度,但通常是更复杂的计算的结果, 例如,对客户端的本地数据集采...
[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...
(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。
Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model with training data distributed over a large number of clients each with unreliable and relatively slow network connections. We consider learning algorithms for this setting where on each round...
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
[3]. Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon.Federated Learning: Strategies for Improving Communication Efficiency.2016 [4]. Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong.Federated Machine Learning: Concept and Applications.20...
Bacon (2016) Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492. [25] G. Lee, Y. Shin, M. Jeong, and S. Yun (2021) Preservation of the global knowledge by not-true self knowledge distillation in federated learning. arXiv preprint arXiv:...
[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...