Augmented Deep Reinforcement Learning for Online Energy Minimization of Wireless Powered Mobile Edge Computing

Chen, Xiaojing; Dai, Weiheng; Ni, Wei; Wang, Xin; Zhang, Shunqing; Xu, Shugong; Sun, Yanzan

doi:10.1109/tcomm.2023.3251353

Cited by 14 publications

(2 citation statements)

References 35 publications

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“…According to different communication ranges and data capacities [57], [61], intelligent marketing [62], intelligent manufacturing [63], smart home [64], intelligent security [65], and intelligent health [66]. Machine-learning algorithms also contribute considerably to wireless communication development [67] and failure analysis [68]. Efforts have been made to train a machine-learning model that can be applied to optimize resource allocation in wireless networks and improve overall network efficiency [69].…”

Section: A Overview Of Wireless Communications Technologiesmentioning

confidence: 99%

“…The wireless network enables a flexible and convenient way of transmitting data from local devices for machine learning applications, such as intelligent transport[59]-[61], intelligent marketing[62], intelligent manufacturing[63], smart home[64], intelligent security[65], and intelligent health[66]. Machine-learning algorithms also contribute considerably to wireless communication development[67] and failure analysis[68]. Efforts have been made to train a machine-learning model that can be…”

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confidence: 99%

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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)

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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.

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