Search Results for author: Deniz Gündüz

Found 34 papers, 5 papers with code

Secure Deep-JSCC Against Multiple Eavesdroppers

no code implementations5 Aug 2023 Seyyed AmirHossein Ameli Kalkhoran, Mehdi Letafati, Ecenaz Erdemir, Babak Hossein Khalaj, Hamid Behroozi, Deniz Gündüz

Adversarial accuracy of eavesdroppers are also studied over Rayleigh fading, Nakagami-m, and AWGN channels to verify the generalization of the proposed scheme.

Ensemble Learning

Sustainable Edge Intelligence Through Energy-Aware Early Exiting

no code implementations23 May 2023 Marcello Bullo, Seifallah Jardak, Pietro Carnelli, Deniz Gündüz

However, due to their computational complexity, DL models consume significant amounts of energy, which can rapidly drain the battery and compromise the performance of IoT devices.

Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference

no code implementations17 Apr 2023 Haotian Wu, Nitish Mital, Krystian Mikolajczyk, Deniz Gündüz

We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object, which are then used jointly to retrieve similar images at the edge server over a shared multiple access channel.

Contrastive Learning Image Retrieval +1

Collaborative Semantic Communication for Edge Inference

no code implementations10 Jan 2023 Wing Fei Lo, Nitish Mital, Haotian Wu, Deniz Gündüz

We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object from different angles and locations, which are then used jointly to retrieve similar images at the edge server over a shared multiple access channel (MAC).

Image Retrieval Retrieval

Device Selection for the Coexistence of URLLC and Distributed Learning Services

no code implementations22 Dec 2022 Milad Ganjalizadeh, Hossein Shokri Ghadikolaei, Deniz Gündüz, Marina Petrova

Our simulation results confirm that our solution can significantly decrease the training delay of the distributed AI service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all network resources.

MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles

no code implementations7 Dec 2022 Bowen Xie, Yuxuan Sun, Sheng Zhou, Zhisheng Niu, Yang Xu, Jingran Chen, Deniz Gündüz

Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities.

Federated Learning Trajectory Prediction

Over-the-Air Federated Edge Learning with Hierarchical Clustering

no code implementations19 Jul 2022 Ozan Aygün, Mohammad Kazemi, Deniz Gündüz, Tolga M. Duman

Our scheme utilizes OTA cluster aggregations for the communication of the MUs with their corresponding IS, and OTA global aggregations from the ISs to the PS.

Clustering Federated Learning

A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming

1 code implementation21 Jun 2022 Zhixiong Yang, Jing-Yuan Xia, Junshan Luo, Shuanghui Zhang, Deniz Gündüz

This paper proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming.

Semi-Decentralized Federated Learning with Collaborative Relaying

no code implementations23 May 2022 Michal Yemini, Rajarshi Saha, Emre Ozfatura, Deniz Gündüz, Andrea J. Goldsmith

We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS).

Federated Learning

Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication

no code implementations8 May 2022 Yang Wang, Zhen Gao, Dezhi Zheng, Sheng Chen, Deniz Gündüz, H. Vincent Poor

It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks.

Rate-Constrained Remote Contextual Bandits

no code implementations26 Apr 2022 Francesco Pase, Deniz Gündüz, Michele Zorzi

We consider a rate-constrained contextual multi-armed bandit (RC-CMAB) problem, in which a group of agents are solving the same contextual multi-armed bandit (CMAB) problem.

Marketing Multi-Armed Bandits

Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs

no code implementations20 Mar 2022 Francesc Wilhelmi, Jernej Hribar, Selim F. Yilmaz, Emre Ozfatura, Kerem Ozfatura, Ozlem Yildiz, Deniz Gündüz, Hao Chen, Xiaoying Ye, Lizhao You, Yulin Shao, Paolo Dini, Boris Bellalta

As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency.

Federated Learning

Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning

no code implementations17 Feb 2022 Yuxuan Sun, Sheng Zhou, Zhisheng Niu, Deniz Gündüz

In this work, we propose time-correlated sparsification with hybrid aggregation (TCS-H) for communication-efficient FEEL, which exploits jointly the power of model compression and over-the-air computation.

Federated Learning Model Compression +1

Cost-Efficient Distributed Learning via Combinatorial Multi-Armed Bandits

no code implementations16 Feb 2022 Maximilian Egger, Rawad Bitar, Antonia Wachter-Zeh, Deniz Gündüz

We consider the distributed SGD problem, where a main node distributes gradient calculations among $n$ workers.

Multi-Armed Bandits

Hierarchical Over-the-Air Federated Edge Learning

no code implementations21 Dec 2021 Ozan Aygün, Mohammad Kazemi, Deniz Gündüz, Tolga M. Duman

Federated learning (FL) over wireless communication channels, specifically, over-the-air (OTA) model aggregation framework is considered.

Federated Learning

DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding

no code implementations25 Nov 2021 Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski, Deniz Gündüz

Recent works have shown that the task of wireless transmission of images can be learned with the use of machine learning techniques.

DeepWiVe: Deep-Learning-Aided Wireless Video Transmission

no code implementations25 Nov 2021 Tze-Yang Tung, Deniz Gündüz

We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform.

MS-SSIM Reinforcement Learning (RL) +2

Coded Computation across Shared Heterogeneous Workers with Communication Delay

no code implementations23 Sep 2021 Yuxuan Sun, Fan Zhang, Junlin Zhao, Sheng Zhou, Zhisheng Niu, Deniz Gündüz

In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to workers for parallel computation.

Distributed Computing

Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling

no code implementations14 Sep 2021 Mehmet Emre Ozfatura, Junlin Zhao, Deniz Gündüz

We consider federated edge learning (FEEL) over wireless fading channels taking into account the downlink and uplink channel latencies, and the random computation delays at the clients.

Fairness Scheduling

Variational Leakage: The Role of Information Complexity in Privacy Leakage

1 code implementation5 Jun 2021 Amir Ahooye Atashin, Behrooz Razeghi, Deniz Gündüz, Slava Voloshynovskiy

We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer.

Face Recognition Privacy Preserving Deep Learning +2

Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints

no code implementations31 May 2021 Yuxuan Sun, Sheng Zhou, Zhisheng Niu, Deniz Gündüz

In this work, we consider an over-the-air FEEL system with analog gradient aggregation, and propose an energy-aware dynamic device scheduling algorithm to optimize the training performance under energy constraints of devices, where both communication energy for gradient aggregation and computation energy for local training are included.


Quantifying and Localizing Usable Information Leakage from Neural Network Gradients

no code implementations28 May 2021 Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Soteris Demetriou, Deniz Gündüz, Hamed Haddadi

Our proposed framework enables clients to localize and quantify the private information leakage in a layer-wise manner, and enables a better understanding of the sources of information leakage in collaborative learning, which can be used by future studies to benchmark new attacks and defense mechanisms.

Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs

1 code implementation25 May 2021 Mohammad Malekzadeh, Anastasia Borovykh, Deniz Gündüz

It is known that deep neural networks, trained for the classification of non-sensitive target attributes, can reveal sensitive attributes of their input data through internal representations extracted by the classifier.

Knowledge Distillation

Dopamine: Differentially Private Federated Learning on Medical Data

1 code implementation27 Jan 2021 Mohammad Malekzadeh, Burak Hasircioglu, Nitish Mital, Kunal Katarya, Mehmet Emre Ozfatura, Deniz Gündüz

While rich medical datasets are hosted in hospitals distributed across the world, concerns on patients' privacy is a barrier against using such data to train deep neural networks (DNNs) for medical diagnostics.

Federated Learning

Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding

no code implementations26 Sep 2020 David Burth Kurka, Deniz Gündüz

We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels.

DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback

4 code implementations25 Nov 2019 David Burth Kurka, Deniz Gündüz

It is well known that separation is not optimal in the practical finite blocklength regime; however, there are no known practical joint source-channel coding (JSCC) schemes that can exploit the feedback signal and surpass the performance of separation-based schemes.

Energy-Aware Analog Aggregation for Federated Learning with Redundant Data

no code implementations1 Nov 2019 Yuxuan Sun, Sheng Zhou, Deniz Gündüz

In this work, we consider analog aggregation to scale down the communication cost with respect to the number of workers, and introduce data redundancy to the system to deal with non-i. i. d.

Federated Learning Scheduling

Deep Convolutional Compression for Massive MIMO CSI Feedback

no code implementations2 Jul 2019 Qianqian Yang, Mahdi Boloursaz Mashhadi, Deniz Gündüz

In comparison with previous works, the main contributions of DeepCMC are two-fold: i) DeepCMC is fully convolutional, and it can be used in a wide range of scenarios with various numbers of sub-channels and transmit antennas; ii) DeepCMC includes quantization and entropy coding blocks and minimizes a cost function that accounts for both the rate of compression and the reconstruction quality of the channel matrix at the BS.


A Reinforcement Learning Approach to Age of Information in Multi-User Networks

no code implementations1 Jun 2018 Elif Tuğçe Ceran, Deniz Gündüz, András György

Scheduling the transmission of time-sensitive data to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users under a constraint on the average number of transmissions at the source node.

reinforcement-learning Reinforcement Learning (RL) +1

The Multi-layer Information Bottleneck Problem

no code implementations14 Nov 2017 Qianqian Yang, Pablo Piantanida, Deniz Gündüz

Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variable, quantified by the mutual information.

Content-Level Selective Offloading in Heterogeneous Networks: Multi-armed Bandit Optimization and Regret Bounds

no code implementations23 Jul 2014 Pol Blasco, Deniz Gündüz

It is shown that the proposed algorithms learn the popularity profile quickly for a wide range of system parameters.

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