ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2012.04436
  4. Cited By
Towards Communication-efficient and Attack-Resistant Federated Edge
  Learning for Industrial Internet of Things

Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things

8 December 2020
Yi Liu
Ruihui Zhao
Jiawen Kang
A. Yassine
Dusit Niyato
Jia-Jie Peng
    FedML
ArXivPDFHTML

Papers citing "Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things"

12 / 12 papers shown
Title
Deep Anomaly Detection for Time-series Data in Industrial IoT: A
  Communication-Efficient On-device Federated Learning Approach
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
Yi Liu
S. Garg
Jiangtian Nie
Yan Zhang
Zehui Xiong
Jiawen Kang
M. S. Hossain
FedML
49
378
0
19 Jul 2020
Data Poisoning Attacks Against Federated Learning Systems
Data Poisoning Attacks Against Federated Learning Systems
Vale Tolpegin
Stacey Truex
Mehmet Emre Gursoy
Ling Liu
FedML
97
646
0
16 Jul 2020
Federated Learning for 6G Communications: Challenges, Methods, and
  Future Directions
Federated Learning for 6G Communications: Challenges, Methods, and Future Directions
Yi Liu
Lizhen Qu
Zehui Xiong
Jiawen Kang
Xiaofei Wang
Dusit Niyato
FedML
AI4CE
28
280
0
04 Jun 2020
A Secure Federated Learning Framework for 5G Networks
A Secure Federated Learning Framework for 5G Networks
Yi Liu
Jia-Jie Peng
Jiawen Kang
Abdullah M. Iliyasu
Dusit Niyato
A. El-latif
FedML
22
196
0
12 May 2020
Privacy-preserving Traffic Flow Prediction: A Federated Learning
  Approach
Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
Yi Liu
James Jianqiao Yu
Jiawen Kang
Dusit Niyato
Shuyu Zhang
AI4TS
43
445
0
19 Mar 2020
Federated Variance-Reduced Stochastic Gradient Descent with Robustness
  to Byzantine Attacks
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks
Zhaoxian Wu
Qing Ling
Tianyi Chen
G. Giannakis
FedML
AAML
49
182
0
29 Dec 2019
Abnormal Client Behavior Detection in Federated Learning
Abnormal Client Behavior Detection in Federated Learning
Suyi Li
Yong Cheng
Yang Liu
Wei Wang
Tianjian Chen
AAML
17
134
0
22 Oct 2019
Reliable Federated Learning for Mobile Networks
Reliable Federated Learning for Mobile Networks
Jiawen Kang
Zehui Xiong
Dusit Niyato
Y. Zou
Yang Zhang
Mohsen Guizani
FedML
31
461
0
14 Oct 2019
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using
  Federated XGBoost
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
Mengwei Yang
Linqi Song
Jie Xu
Congduan Li
Guozhen Tan
FedML
85
30
0
16 Jul 2019
Towards Federated Learning at Scale: System Design
Towards Federated Learning at Scale: System Design
Keith Bonawitz
Hubert Eichner
W. Grieskamp
Dzmitry Huba
A. Ingerman
...
H. B. McMahan
Timon Van Overveldt
David Petrou
Daniel Ramage
Jason Roselander
FedML
68
2,652
0
04 Feb 2019
Deep Gradient Compression: Reducing the Communication Bandwidth for
  Distributed Training
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Chengyue Wu
Song Han
Huizi Mao
Yu Wang
W. Dally
91
1,394
0
05 Dec 2017
Federated Learning: Strategies for Improving Communication Efficiency
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konecný
H. B. McMahan
Felix X. Yu
Peter Richtárik
A. Suresh
Dave Bacon
FedML
243
4,620
0
18 Oct 2016
1