3 citations · 10 across the 6 of their papers we have counts for
9 papers
Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) communication networks through the integration of m…
Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution
Yalin E. Sagduyu, Tugba Erpek, Yi Shi
This paper studies the poisoning attack and defense interactions in a federated learning (FL) system, specifically in the context of wireless signal classification using deep learn…
Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning
Yalin E. Sagduyu, Tugba Erpek
Low-Power Wide-Area Network (LPWAN) technologies, such as LoRa, have gained significant attention for their ability to enable long-range, low-power communication for Internet of Th…
Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener +1
This paper studies task-oriented, otherwise known as goal-oriented, communications, in a setting where a transmitter communicates with multiple receivers, each with its own task to…
Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks
Yi Shi, Yalin E. Sagduyu, Tugba Erpek
Decentralized federated learning (DFL) is an effective approach to train a deep learning model at multiple nodes over a multi-hop network, without the need of a server having direc…
End-to-End Autoencoder Communications with Optimized Interference Suppression
Kemal Davaslioglu, Tugba Erpek, Yalin E. Sagduyu
An end-to-end communications system based on Orthogonal Frequency Division Multiplexing (OFDM) is modeled as an autoencoder (AE) for which the transmitter (coding and modulation) a…