Results 11 to 20 of about 17,541 (167)

Anarchic Federated Learning

CoRR, 2021
Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabilities, which call for flexible worker participation in terms of timing, effort, data heterogeneity, etc. To satisfy the need for flexible worker participation, we consider a new
Haibo Yang 0001, Xin Zhang 0054, Prashant Khanduri, Jia Liu 0002   +3 more
openaire   +3 more sources

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

IEEE Access, 2021
In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based
Mohamed Amine Ferrag, Othmane Friha, Leandros Maglaras, Helge Janicke, Lei Shu   +4 more
doaj   +1 more source

DWFed: A statistical- heterogeneity-based dynamic weighted model aggregation algorithm for federated learning

Frontiers in Neurorobotics, 2022
Federated Learning is a distributed machine learning framework that aims to train a global shared model while keeping their data locally, and previous researches have empirically proven the ideal performance of federated learning methods. However, recent
Aiguo Chen, Yang Fu, Lingfu Wang, Guiduo Duan   +3 more
doaj   +1 more source

Preconditioned Federated Learning

CoRR, 2023
Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner. The standard optimization method in FL is Federated Averaging (FedAvg), which performs multiple local SGD steps between communication rounds.
Zeyi Tao, Jindi Wu, Qun Li 0001
openaire   +2 more sources

Heterogeneous Federated Learning

CoRR, 2020
Full version [Fed2: Feature-Aligned Federated Learning] accepted in KDD ...
Fuxun Yu, Weishan Zhang, Zhuwei Qin, Zirui Xu, Di Wang 0003, Chenchen Liu, Zhi Tian, Xiang Chen 0010   +7 more
openaire   +2 more sources

Sustainable Federated Learning

CoRR, 2021
Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems. In this paper, we introduce sustainable machine learning in federated learning settings, using rechargeable devices that can collect energy from the ambient environment.
Basak Guler, Aylin Yener
openaire   +2 more sources

Hybrid Federated and Centralized Learning [PDF]

2021 29th European Signal Processing Conference (EUSIPCO), 2021
Many of the machine learning (ML) tasks are focused on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) leading to a huge communication overhead. Federated learning (FL) overcomes this issue by allowing the clients to send only the model updates to the PS instead of the whole ...
Ahmet M. Elbir, Sinem Coleri, Kumar Vijay Mishra   +2 more
openaire   +2 more sources

Green Federated Learning

CoRR, 2023
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets. As a consequence, the amount of compute used in training state-of-the-art models is exponentially increasing (doubling every 10 months between 2015 and 2022), resulting in a large carbon footprint.
Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk-Willem Krüger, Michael G. Rabbat, Carole-Jean Wu, Ilya Mironov   +9 more
openaire   +2 more sources

The Cost of Training Machine Learning Models Over Distributed Data Sources

IEEE Open Journal of the Communications Society, 2023
Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data. However, it requires a central server to
Elia Guerra, Francesc Wilhelmi, Marco Miozzo, Paolo Dini   +3 more
doaj   +1 more source

Review on application progress of federated learning model and security hazard protection

Digital Communications and Networks, 2023
Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.
Aimin Yang, Zezhong Ma, Chunying Zhang, Yang Han, Zhibin Hu, Wei Zhang, Xiangdong Huang, Yafeng Wu   +7 more
doaj   +1 more source