federated learning in mobile edge networks: a comprehensive survey

Federated Learning in Mobile Edge Networks: A Comprehensive Survey Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, Chunyan Miao (Submitted on 26 Sep 2019) In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Google Scholar Cross Ref; Tao Lin, Lingjing Kong, Sebastian U. Stich, and Martin Jaggi. Surv. (Federated Learning in Mobile Edge Networks) A Comprehensive Survey AI Survey Posted on April 21, 2021. Therefore, it is crucial to find trustworthy and reliable clients in . B. Lim is with Alibaba Group and the Alibaba-NTU Joint Research Institute, Nanyang Technological . In federated learning, mobile devices may upload unreliable data, which could cause that the server fail to aggregate the global model. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Traditional cloudbased Machine Learning (ML . 向彪-blockchain: 大佬666，获益匪浅，灵感如泉涌般一发不可收拾! IEEE Commun. 이로인해 ML 모델의 학습은 모바일 edge network에서 수행될 수 있다. (Survey 13) Federated Learning in Mobile Edge Networks: A Comprehensive Survey AlexanderChen 香港理工大学 计算机博士在读 I. Federated Learning in Mobile Edge Networks: A Comprehensive Survey Wei Yang Bryan Lim, Nguyen Cong Luong, +5 authors C. Miao Published 26 September 2019 Computer Science IEEE Communications Surveys & Tutorials In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. The advantages of low delay of 5G network should be better utilized in the vehicle-road cooperative system. Federated Learning in Mobile Edge Networks: A Comprehensive Survey 26 Sep 2019 . Proceeding: 2020 Authors: Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, Chunyan Miao . Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. Federated learning can effectively protect local data privacy in 5G-V2X environment and ensure data protection in Internet of vehicles environment. 2020. . 2031 - 2063 CrossRef View Record in Scopus Google Scholar [arXiv 2020] Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective [IEEE Signal Processing Magazine 2020] Federated Learning: Challenges, Methods, and Future Directions [IEEE Communications Surveys & Tutorials 2020] Federated Learning in Mobile Edge Networks A Comprehensive Survey . For the reader's convenience, we classify the related studies to be discussed in this survey in Fig. Tutor. APPLICATIONS OF FEDERATED LEARNING FOR MOBILE EDGE COMPUTING. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. B. Surv. In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. The end devices then send the model updates rather than raw data to the server for aggregation. Federated learning in mobile edge networks: a comprehensive survey IEEE Commun Surv Tutor , 22 ( 3 ) ( 2020 ) , pp. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data . Mohammad et al. Deep Learning B. Federated Learning C. Statistical Challenges of FL D. FL protocols and frameworks E. Unique characteristics and issues of FL III. Edge Caching and Computation Offloading. Federated Learning in Mobile Edge Networks: A Comprehensive Survey. Tutor. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. Age-based scheduling policy for federated learning in mobile edge networks. Federated learning in mobile edge networks: A comprehensive survey. However, it is not permitted to use DR-NTU works for (a) commercial purposes, (b) the creation of a database or (c . Federated Learning in Mobile Edge Networks: A Comprehensive Survey. 3 (2020), 2031--2063. Download Form: Federated learning in mobile edge networks : a comprehensive survey. 1 Federated Learning in Mobile Edge Networks: A Comprehensive Survey Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Fellow, IEEE, Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. 보존 되는 것을 보증하기 위해 분산된 디바이스에서 학습하는 . The end devices then send the model updates rather than raw data to the server for aggregation. In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. A. Arafa, T. Quek, and H. Poor. D. Vehicular Networks. 전통적인 cloud-centric training 접근방식과 비교하여 FL은 아래와 같은 강점을 지닌다. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., … Miao, C. (2020). BACKGROUND AND FUNDAMENTALS OF FEDERATED LEARNING A. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. vey in [15] focuses on FL for mobile-edge networks while highlighting the challenges related to communication cost, resources, privacy and security. C. Base Station Association. A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond . In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. CHALLENGES AND FUTURE RESEARCH DIRECTIONS. In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Unless otherwise specified, all works in DR-NTU can be viewed and downloaded by users for their own research, private study and teaching purposes. VII. In Proceedings of . Federated Learning in Mobile Edge Networks: A Comprehensive Survey Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Fellow, IEEE, Qiang Yang, Fellow, IEEE, Dusit Niyato, Fellow, IEEE, and Chunyan Miao Abstract—In recent years, mobile devices are equipped with Lim WYB, Luong NC, Hoang DT, Jiao Y, Liang YC, Yang Q, Niyato D, Miao C (2020) Federated learning in mobile edge networks: A comprehensive survey. Federated Learning in Mobile Edge Networks: A Comprehensive Survey. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Wei Yang Bryan Lim, etc. Federated Learning in Mobile Edge Networks: A Comprehensive Survey Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Dropped participants. The feature of federal learning is that the client continuously updates the parameters and the server aggregates the parameters. 그리고 오로지 학습된 모델의 weight만을 FL 서버로 전송한다. DOI: 10.1109/COMST.2020.2986024 Corpus ID: 202888951; Federated Learning in Mobile Edge Networks: A Comprehensive Survey @article{Lim2020FederatedLI, title={Federated Learning in Mobile Edge Networks: A Comprehensive Survey}, author={Wei Yang Bryan Lim and Nguyen Cong Luong and Dinh Thai Hoang and Yutao Jiao and Ying-Chang Liang and Qiang Yang and Dusit Tao Niyato and Chunyan Miao}, journal . VI. However, there are several challenges to implementing FL at scale. In FL, end devices use their local. 2020 22 3 2031 2063 10.1109/COMST.2020.2986024 Google Scholar Cross Ref Lim, W. Y. Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Fellow, IEEE, Qiang Yang, Fellow, IEEE, Dusit Niyato, Fellow, IEEE, and Chunyan Miao W. Y. FL 에서는 모바일 디바이스는 그들의 local data를 사용하여 FL 서버와 협동적으로 학습을 수행한다. In FL, end devices use their local data to train an ML model required by the server. Lim WYB et al. In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. studied the application of federated learning in wireless network and edge computing, . Federated Learning in Mobile Edge Networks: A Comprehensive Survey. 1 Asynchronous Task Allocation for Federated and Parallelized Mobile Edge Learning Umair Mohammad, Student Member, IEEE, Sameh Sorour, Senior Member, IEEE Abstract—This paper proposes a scheme to efficiently execute orchestrator waits for all learners to complete an equal number distributed learning tasks in an asynchronous manner while of iterations of the ML training algorithm and hence . 2020. Recently, in light of increasingly stringent data privacy legislation and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Federated Learning in Mobile Edge Networks: A Comprehensive Survey. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Federated Learning in Mobile Edge Networks: A Comprehensive Survey Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Fellow, IEEE, Qiang Yang, Fellow, IEEE, Dusit Niyato, Fellow, IEEE, and Chunyan Miao Abstract—In recent years, mobile devices are equipped with Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. Federated Learning in Mobile Edge Networks: A Comprehensiv e Survey W ei Y ang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Y utao Jiao, Y ing-Chang Liang, F ellow, IEEE , As such, in this survey, we also consider FL's potential to serve as an enabling technology for optimizing mobile edge networks, e.g., in cell association [hamidouche2018collaborative], computation offloading [wang2018edge], and vehicular networks [samarakoon2018distributed]. However, FL by itself does not provide the levels of security and robustness required by today’s standards in distributed autonomous systems. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. IEEE Commun Surv Tutorials 22(3):2031-2063. 16 PDF View 1 excerpt, cites background Most of the . provide a comprehensive But the existing asynchronous federated learning obtains a local model through different node training and completes the update of the . Mobile edge computing (MEC) provides an effective solution to help the Internet of Things (IoT) devices with delay-sensitive and computation-intensive tasks by offering computing capabilities in the proximity of mobile device users. Federated Learning in Mobile Edge Networks: A Comprehensive Survey Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, Chunyan Miao In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. INTRODUCTION II. In FL, end devices use their local data to train an ML model required by the server. 학습 데이터가 개인 디바이스에 보존 되는 것을 보증하기 위해 분산된 디바이스에서 학습하는 decentralized ML 방식인 Federated Learning (FL)이 등장하였다. Traditional cloudbased Machine Learning (ML . However, it is not permitted to use DR-NTU works for (a) commercial purposes, (b) the creation of a database or (c . In a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved, this raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. Download Form: Federated learning in mobile edge networks : a comprehensive survey. Researchers have proposed many ways to aggregate parameters on the server, such as federated averaging (FedAvg) [] and federated stochastic variance reduced gradient (FSVRG) [].In the rest of this paper, we use FedAvg as parameters aggregation method. A. Cyberattack Detection. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. A survey on federated learning. 이 과정은 원하는 정확도에 도달할때 까지 여러번 수행한다. Coupled with advancements in Deep Learning (DL . Article Google Scholar Nishio T, Yonetani R (2019) Client selection for federated learning with heterogeneous resources in mobile edge. . Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Recently, in light of increasingly stringent data privacy legislation and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. Federated Learning in Mobile Edge Networks: A Comprehensive Survey. 2. Edit social preview In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Federated Learning in Mobile Edge Networks: A Comprehensive Survey. Federated learning in mobile edge networks: a comprehensive survey IEEE Commun. Surv. COMMUNICATION COST Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular . Unless otherwise specified, all works in DR-NTU can be viewed and downloaded by users for their own research, private study and teaching purposes. 1 Federated Learning in Mobile Edge Networks: A Comprehensive Survey Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Fellow, IEEE, Distributed autonomous systems asynchronous federated Learning with heterogeneous resources in mobile edge Networks: a Comprehensive ieee... Arafa, T. Quek, and Martin Jaggi end devices use their local data to an. 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