Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications

Raza, Muhammad; Lipman, Justin; Shariati, Negin; Ni, Wei; Jamalipour, Abbas

doi:10.1109/tvt.2022.3155719

Cited by 7 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This inconsistency can lead to data corruption or loss, and an attacker might exploit the inconsistency to inject malicious backdoor attacks. On the other hand, machine learning has demonstrated its effectiveness in transmission resource allocation under both centralized settings [83], semi-distributed settings [84], and distributed settings [85]- [87]. • Scalability and Management: WFL is designed to be highly scalable and to deal with a large number of nodes (devices).…”

Section: Attack Roundmentioning

confidence: 99%

Defense Strategies Toward Model Poisoning Attacks in Federated Learning: A Survey

Wang

Qiao

Zhang

et al. 2022

2022 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

Due to the greatly improved capabilities of devices, massive data, and increasing concern about data privacy, Federated Learning (FL) has been increasingly considered for applications to wireless communication networks (WCNs). Wireless FL (WFL) is a distributed method of training a global deep learning model in which a large number of participants each train a local model on their training datasets and then upload the local model updates to a central server. However, in general, nonindependent and identically distributed (non-IID) data of WCNs raises concerns about robustness, as a malicious participant could potentially inject a "backdoor" into the global model by uploading poisoned data or models over WCN. This could cause the model to misclassify malicious inputs as a specific target class while behaving normally with benign inputs. This survey provides a comprehensive review of the latest backdoor attacks and defense mechanisms. It classifies them according to their targets (data poisoning or model poisoning), the attack phase (local data collection, training, or aggregation), and defense stage (local training, before aggregation, during aggregation, or after aggregation). The strengths and limitations of existing attack strategies and defense mechanisms are analyzed in detail. Comparisons of existing attack methods and defense designs are carried out, pointing to noteworthy findings, open challenges, and potential future research directions related to security and privacy of WFL.

show abstract