简要概述了联邦学习(Federated Learning, FL)的概念和研究意义。FL是一种分布式机器学习方法,旨在在远程设备或数据中心(如手机或医院)上训练统计模型,同时保持数据本地化,不将数据上传至中央服务器。这种学习方法在保护数据隐私的同时,也应对了大规模异构网络(如智能手机、物联网设备)的挑战。 由于FL需要在数量庞大且...
联邦学习(Federated Learning)在大数据时代逐渐兴起,主要是为了解决数据隐私问题。随着数据隐私的重要性不断提升,人们越来越不愿意将自己的敏感信息交给中央服务器处理。联邦学习通过在用户设备本地训练模型,并将每个客户端的模型参数而非原始数据进行汇总,旨在保护用户隐私。 尽管联邦学习具有一定的隐私保护能力,但其分布式...
Federated learning (FL) is a distributed learning paradigm that preserves users' data privacy while leveraging the entire dataset of all participants. In FL, multiple models are trained independently on the clients and aggregated centrally to update a global model in an iterative process. Although ...
其他更具原则性的方法包括不可知联邦学习(Agnostic Federated Learning)[80],它通过minimax优化方案优化由客户机分布混合形成的任何目标分布的集中模型。Li等人[66]采取了另一种更普遍的方法,提出了一个被称为q-FFL的目标,在该目标中,具有较高损失的设备被赋予较高的相对权重,以鼓励在最终精度分布中减少方差。除了公...
In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities. 展开 ...
Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges Authors Solmaz Niknam, Harpreet S. Dhillon, Jeffrey H. Reed
0摘要-联邦学习开山之作Communication-Efficient Learning of Deep Networks from Decentralized junhaofu "你有没有发现:只要你一整天都很认真的学,少用手机,完成该完成的任务,运动半小时出点汗,内心就会感到平静和快乐,也不会再胡思乱想,患得患失,焦虑不已了" ...
Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges 1 INTRODUCTION 對於無線通訊,採用ML進行系統設計和分析尤其具有吸引力,因為傳統模型驅動的方法不夠豐富以應對現代無線網路不斷增長的複雜度與異構性。單獨利用資料分析(例如被使用在模型驅動的通訊系統設計中)的一種替代選擇是使用...
One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In ...
关键词: Learning systems; Ubiquitous computing; 6g; AI applications; Computing units; Edge computing; Edge data; Edge intelligence; Federated learning; Intelligent transformations; Service innovation; Terminal ecosystems; Digital storage; 年份: 2022 收藏 ...