A Critical Evaluation of Privacy and Security Threats in Federated Learning
With the advent of smart devices, smartphones, and smart everything, the Internet of Things (IoT) has emerged with an incredible impact on the industries and human life. The IoT consists of millions of clients that exchange massive amounts of critical data, which results in high privacy risks when p...
Main Authors: | Muhammad Asad, Ahmed Moustafa, Chao Yu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-12-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/24/7182 |
Similar Items
-
Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
by: Pengrui Liu, et al.
Published: (2022-02-01) -
Survey on vertical federated learning: algorithm, privacy and security
by: Jinyin CHEN, et al.
Published: (2023-04-01) -
Survey on vertical federated learning: algorithm, privacy and security
by: Jinyin CHEN, Rongchang LI, Guohan HUANG, Tao LIU, Haibin ZHENG, Yao CHENG
Published: (2023-04-01) -
Federated Learning Based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks
by: Sheikh Imroza Manzoor, et al.
Published: (2023-01-01) -
A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures
by: Muath A. Obaidat, et al.
Published: (2020-05-01)