FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things

Citations

WEB OF SCIENCE

82

초록

This letter presents a federated learning-basd data-accumulation scheme that combines drones and blockchain for remote regions where Internet of Things devices face network scarcity and potential cyber threats. The scheme contains a two-phase authentication mechanism in which requests are first validated using a cuckoo filter, followed by a timestamp nonce. Secure accumulation is achieved by validating models using a Hampel filter and loss checks. To increase the privacy of the model, differential privacy is employed before sharing. Finally, the model is stored in the blockchain after consent is obtained from mining nodes. Experiments are performed in a proper environment, and the results confirm the feasibility of the proposed scheme.

키워드

Drones; Blockchains; Data models; Training; Servers; Logic gates; Authentication; Blockchain; dew computing; differential privacy; drone; federated learning; Internet of Things; long short-term memory
제목
FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things
저자
Islam, Anik; Al Amin, Ahmed; Shin, Soo Young
DOI
10.1109/LWC.2022.3151873
발행일
2022-05
유형
Article
저널명
IEEE Wireless Communications Letters
권
11
호
5
페이지
972 ~ 976