Special Section: AI-Driven Security Solutions for the Internet of Everything RR-LADP: A PrivacyEnhanced Federated Learning Scheme for Internet of Everything Zerui Li, Yuchen Tian, and Qing Liao Harbin Institute of Technology (Shenzhen) Weizhe Zhang and Yang Liu Harbin Institute of Technology (Shenzhen) Peng Cheng Laboratory Xiaojiang Du Temple University Mohsen Guizani Qatar University Abstract-While thewidespread use of ubiquitously connected devices in Internet of Everything (IoE) offers enormous benefits, it also raises serious privacy concerns. Federated learning, as one of the promising solutions to alleviate such problems, is considered as capable of performing data trainingwithout exposing raw data that kept by multiple devices. However, either malicious attackers or untrusted servers can deduce users' privacy from the local updates of each device. Previous studies mainly focus on privacy-preserving approaches inside the servers, which require the framework to be built on trusted servers. In this article, we propose a privacy-enhanced federated learning scheme for IoE. Two mechanisms are adopted in our approach, namely the randomized response (RR) mechanism and the local adaptive differential privacy (LADP) mechanism. RR is adopted to prevent the server from knowing whose updates are collected in each round. LADP enables devices to add noise adaptively to its local updates before submitting them to the server. Experiments demonstrate the feasibility and effectiveness of our approach. Digital Object Identifier 10.1109/MCE.2021.3059958 Date ofpublication 17February 2021; date ofcurrent version 5August 2021. September/October 2021 Published by the IEEE Consumer Technology Society 2162-2248 ß 2021 IEEE 93