Search Results for author: Aylin Yener

Found 17 papers, 0 papers with code

Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces

no code implementations22 Jan 2024 Jiayu Mao, Aylin Yener

Over-the-air federated learning (OTA-FL) provides bandwidth-efficient learning by leveraging the inherent superposition property of wireless channels.

Multi-Task Learning Personalized Federated Learning

Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing

no code implementations3 Jan 2024 Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) communication networks through the integration of multi-task learning.

Federated Learning Multi-Task Learning +1

Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access

no code implementations21 Dec 2023 Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

Recognizing the computational constraints and trust issues associated with on-device computation, we propose a collaborative system wherein the edge device communicates selectively processed information to a trusted receiver acting as a fusion center, where a decision is made to identify whether a potential transmitter is present, or not.

Decoder Image Classification

Joint Sensing and Semantic Communications with Multi-Task Deep Learning

no code implementations8 Nov 2023 Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

The transmitter employs a deep neural network, namely an encoder, for joint operations of source coding, channel coding, and modulation, while the receiver utilizes another deep neural network, namely a decoder, for joint operations of demodulation, channel decoding, and source decoding to reconstruct the data samples.

Decoder Multi-Task Learning

Semantic Text Compression for Classification

no code implementations19 Sep 2023 Emrecan Kutay, Aylin Yener

We study semantic compression for text where meanings contained in the text are conveyed to a source decoder, e. g., for classification.

Decoder Quantization +6

Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning

no code implementations14 Aug 2023 Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus

A multi-task deep learning approach that involves training a common encoder at the transmitter and individual decoders at the receivers is presented for joint optimization of completing multiple tasks and communicating with multiple receivers.

Image Classification Multi-Task Learning

Age of Information in Deep Learning-Driven Task-Oriented Communications

no code implementations11 Jan 2023 Yalin E. Sagduyu, Sennur Ulukus, Aylin Yener

This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter.

Decoder

Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks

no code implementations21 Dec 2022 Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus, Aylin Yener

The backdoor attack can effectively change the semantic information transferred for the poisoned input samples to a target meaning.

Backdoor Attack Decoder

Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks

no code implementations20 Dec 2022 Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus, Aylin Yener

By augmenting the reconstruction loss with a semantic loss, the two deep neural networks (DNNs) of this encoder-decoder pair are interactively trained with the DNN of the semantic task classifier.

Decoder

Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects

no code implementations19 Dec 2022 Yalin E. Sagduyu, Sennur Ulukus, Aylin Yener

In this paper, wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label.

Decoder

Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control

no code implementations12 May 2022 Haibo Yang, Peiwen Qiu, Jia Liu, Aylin Yener

In order to fully utilize this advantage while providing comparable learning performance to conventional federated learning that presumes model aggregation via noiseless channels, we consider the joint design of transmission scaling and the number of local iterations at each round, given the power constraint at each edge device.

Federated Learning

Secure Joint Communication and Sensing

no code implementations22 Feb 2022 Onur Günlü, Matthieu Bloch, Rafael F. Schaefer, Aylin Yener

For independent and identically distributed states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed.

Attribute

Autoencoder-based Communications with Reconfigurable Intelligent Surfaces

no code implementations8 Dec 2021 Tugba Erpek, Yalin E. Sagduyu, Ahmed Alkhateeb, Aylin Yener

This paper presents a novel approach for the joint design of a reconfigurable intelligent surface (RIS) and a transmitter-receiver pair that are trained together as a set of deep neural networks (DNNs) to optimize the end-to-end communication performance at the receiver.

Decoder

Sustainable Federated Learning

no code implementations22 Feb 2021 Basak Guler, Aylin Yener

Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems.

BIG-bench Machine Learning Federated Learning

Energy-Harvesting Distributed Machine Learning

no code implementations10 Feb 2021 Basak Guler, Aylin Yener

This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks.

BIG-bench Machine Learning Edge-computing

QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning

no code implementations13 Oct 2019 Nof Abuzainab, Tugba Erpek, Kemal Davaslioglu, Yalin E. Sagduyu, Yi Shi, Sharon J. Mackey, Mitesh Patel, Frank Panettieri, Muhammad A. Qureshi, Volkan Isler, Aylin Yener

The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks.

reinforcement-learning Reinforcement Learning (RL)

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