中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 学术期刊
  5. International Journal of Automation and Computing
A Weighted Average Consensus Approach for Decentralized Federated Learning

文献类型：期刊论文

作者Alessandro Giuseppi1; Sabato Manfredi2; Antonio Pietrabissa1
刊名Machine Intelligence Research
出版日期2022
卷号19期号:4页码:319-330
关键词Federated learning (FedL) deep learning federated averaging (FedAvg) machine learning (ML) artificial intelligence discrete-time consensus distributed systems
ISSN号2731-538X
DOI10.1007/s11633-022-1338-z
英文摘要Federated learning (FedL) is a machine learning (ML) technique utilized to train deep neural networks (DeepNNs) in a dis tributed way without the need to share data among the federated training clients. FedL was proposed for edge computing and Internet of things (IoT) tasks in which a centralized server was responsible for coordinating and governing the training process. To remove the design limitation implied by the centralized entity, this work proposes two different solutions to decentralize existing FedL algorithms, enabling the application of FedL on networks with arbitrary communication topologies, and thus extending the domain of application of FedL to more complex scenarios and new tasks. Of the two proposed algorithms, one, called FedLCon, is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solu tions, as also shown by the reported validation tests.
源URL[http://ir.ia.ac.cn/handle/173211/55948]  
专题自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位1.Department of Computer, Control, and Management Engineering, University of Rome La Sapienza, Rome 00185, Italy
2.Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples 80125, Italy
推荐引用方式
GB/T 7714		 Alessandro Giuseppi,Sabato Manfredi,Antonio Pietrabissa. A Weighted Average Consensus Approach for Decentralized Federated Learning[J]. Machine Intelligence Research,2022,19(4):319-330.
APA Alessandro Giuseppi,Sabato Manfredi,&Antonio Pietrabissa.(2022).A Weighted Average Consensus Approach for Decentralized Federated Learning.Machine Intelligence Research,19(4),319-330.
MLA Alessandro Giuseppi,et al."A Weighted Average Consensus Approach for Decentralized Federated Learning".Machine Intelligence Research 19.4(2022):319-330.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。