本文内容主要根据预印本网站arXiv上的论文《When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions》结合自己的理解和GPT简要翻译整理,很多地方并不完全按照原文,有…
Future Beyond 5G (B5G)/6G networks will connect a huge amount of devices and will offer innovative services empowered with AI and Machine Learning tools. Nevertheless, private user data, which are essential for training such services, are not an asset that can be unrestrictedly shared over the...
There is an increasing interest in a new machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), and each UE contributes to the learning model by independently computing the gradient based on its local training data. Federat...
Zhang, Michael, et al. "Personalized federated learning with first order model optimization." arXiv preprint arXiv:2012.08565 (2020). 本文是一篇2021年发表在ICRL上的关于个性化联邦学习的文章,该文章赋予了客户一个新的角色,并提出一种新的权重策略,构造了一种在隐私和性能之间进行权衡的名为Fedfomo...
code: https://github.com/felisat/clustered-federated-learning 编辑:古月 目的:传统联邦学习中,存在这个假设: 训练一组模型,使得所有得到用户都能满足最小化风险函数的目标: 假设1 但是,显然这种假设并不是对于所有用户都满足,因为两个用户之间的数据分布很容易是很不相似的,对应于FL中的Non_IID问题。
提出FedAvg算法[1]的论文《Communication-Efficient Learning of Deep Networks from Decentralized Data》较好地解决了统计异构的难点,从而使得FedAvg算法成为了联邦学习的SOTA(state of the art)算法,但该论文仍然有一些不足: 该论文几乎没有考虑系统异构。在实际中,若一些局部模型没能在规定时间内完成训练,系统会丢弃...
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints - felisat/clustered-federated-learning
2016. Federated optimization: Distributed machine learning for on-device intelligence. CoRR abs/1610.02527 (2016). arxiv:1610.02527 http://arxiv.org/abs/1610. 02527 [37] Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016. ...
《2019-IEEE-Motivating workers in federated learning: A stackelberg game perspective》提出一种设备与模型的Stackelberg博弈模型,模型所有者激励拥有设备的工人为本地训练分配更多CPU计算资源,以实现快速收敛。《2021-IEEE-An incentive mechanism for federated learning in wireless cellular network: An auction ...
Industry-Scale Orchestrated Federated Learning for Drug Discovery Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model Layer-Wise Adaptive Model Aggregation for Scalable Fed...