Search Results for author: Mehdi Bennis

Found 83 papers, 7 papers with code

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

1 code implementation8 May 2022 George C. Alexandropoulos, Kyriakos Stylianopoulos, Chongwen Huang, Chau Yuen, Mehdi Bennis, Mérouane Debbah

The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives.

Multi-Armed Bandits

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

no code implementations3 May 2022 Henna Kokkonen, Lauri Lovén, Naser Hossein Motlagh, Juha Partala, Alfonso González-Gil, Ester Sola, Iñigo Angulo, Madhusanka Liyanage, Teemu Leppänen, Tri Nguyen, Panos Kostakos, Mehdi Bennis, Sasu Tarkoma, Schahram Dustdar, Susanna Pirttikangas, Jukka Riekki

We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence.

Time-triggered Federated Learning over Wireless Networks

no code implementations26 Apr 2022 Xiaokang Zhou, Yansha Deng, Huiyun Xia, Shaochuan Wu, Mehdi Bennis

The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner.

Federated Learning

SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks

no code implementations26 Mar 2022 Won Joon Yun, Yunseok Kwak, Hankyul Baek, Soyi Jung, Mingyue Ji, Mehdi Bennis, Jihong Park, Joongheon Kim

However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions.

Distributed Computing Federated Learning

Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks

no code implementations25 Jan 2022 Arian Ahmadi, Omid Semiari, Mehdi Bennis, Merouane Debbah

In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency.

Edge-computing

THz-Empowered UAVs in 6G: Opportunities, Challenges, and Trade-Offs

no code implementations13 Jan 2022 M. Mahdi Azari, Sourabh Solanki, Symeon Chatzinotas, Mehdi Bennis

Envisioned use cases of unmanned aerial vehicles (UAVs) impose new service requirements in terms of data rate, latency, and sensing accuracy, to name a few.

Attention Based Communication and Control for Multi-UAV Path Planning

no code implementations20 Dec 2021 Hamid Shiri, Hyowoon Seo, Jihong Park, Mehdi Bennis

Inspired by the multi-head attention (MHA) mechanism in natural language processing, this letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path planning.

Decision Making

Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding

no code implementations5 Dec 2021 Hankyul Baek, Won Joon Yun, Soyi Jung, Jihong Park, Mingyue Ji, Joongheon Kim, Mehdi Bennis

To address the heterogeneous communication throughput problem, each full-width (1. 0x) SNN model and its half-width ($0. 5$x) model are superposition-coded before transmission, and successively decoded after reception as the 0. 5x or $1. 0$x model depending on the channel quality.

Federated Learning

Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks

no code implementations5 Dec 2021 Hankyul Baek, Won Joon Yun, Yunseok Kwak, Soyi Jung, Mingyue Ji, Mehdi Bennis, Jihong Park, Joongheon Kim

By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput.

Federated Learning

Learning Emergent Random Access Protocol for LEO Satellite Networks

no code implementations3 Dec 2021 Ju-Hyung Lee, Hyowoon Seo, Jihong Park, Mehdi Bennis, Young-Chai Ko

A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems.

Fairness

Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs

no code implementations4 Oct 2021 Beatriz Soret, Lam D. Nguyen, Jan Seeger, Arne Bröring, Chaouki Ben Issaid, Sumudu Samarakoon, Anis El Gabli, Vivek Kulkarni, Mehdi Bennis, Petar Popovski

An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines.

Edge-computing Total Energy

Federated Distributionally Robust Optimization for Phase Configuration of RISs

no code implementations20 Aug 2021 Chaouki Ben Issaid, Sumudu Samarakoon, Mehdi Bennis, H. Vincent Poor

In this article, we study the problem of robust reconfigurable intelligent surface (RIS)-aided downlink communication over heterogeneous RIS types in the supervised learning setting.

Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT

no code implementations17 Aug 2021 Chen-Feng Liu, Mehdi Bennis

While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller's available information, measured by the age of information (AoI), is paramount for high-performing industrial automation.

Federated Learning Model Selection

Semantics-Native Communication with Contextual Reasoning

no code implementations12 Aug 2021 Hyowoon Seo, Jihong Park, Mehdi Bennis, Mérouane Debbah

Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across many tasks.

Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learning

no code implementations12 Jun 2021 Madhusanka Manimel Wadu, Sumudu Samarakoon, Mehdi Bennis

In this article we investigate the problem of client scheduling and resource block (RB) allocation to enhance the performance of model training using FL, over a pre-defined training duration under imperfect channel state information (CSI) and limited local computing resources.

Federated Learning Stochastic Optimization

Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels

no code implementations2 Jun 2021 Mounssif Krouka, Anis Elgabli, Chaouki Ben Issaid, Mehdi Bennis

In this paper, we propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes.

Image Classification Model Compression +1

Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation

no code implementations2 Jun 2021 Mounssif Krouka, Anis Elgabli, Chaouki Ben Issaid, Mehdi Bennis

Split-learning (SL) has recently gained popularity due to its inherent privacy-preserving capabilities and ability to enable collaborative inference for devices with limited computational power.

Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

no code implementations22 May 2021 Won Joon Yun, Byungju Lim, Soyi Jung, Young-Chai Ko, Jihong Park, Joongheon Kim, Mehdi Bennis

In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user.

Graph Attention reinforcement-learning

Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations

no code implementations4 May 2021 Sumudu Samarakoon, Jihong Park, Mehdi Bennis

In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated.

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

no code implementations2 May 2021 Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar

In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and superpositioned samples.

Data Augmentation

Communication-Efficient and Personalized Federated Lottery Ticket Learning

no code implementations26 Apr 2021 Sejin Seo, Seung-Woo Ko, Jihong Park, Seong-Lyun Kim, Mehdi Bennis

The lottery ticket hypothesis (LTH) claims that a deep neural network (i. e., ground network) contains a number of subnetworks (i. e., winning tickets), each of which exhibiting identically accurate inference capability as that of the ground network.

Federated Learning Multi-Task Learning

Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction

no code implementations16 Apr 2021 Abanoub M. Girgis, Hyowoon Seo, Jihong Park, Mehdi Bennis, Jinho Choi

Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power.

UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning

no code implementations2 Apr 2021 Alireza Shamsoshoara, Fatemeh Afghah, Erik Blasch, Jonathan Ashdown, Mehdi Bennis

The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users.

Imitation Learning

Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces

no code implementations31 Mar 2021 Mehdi Naderi Soorki, Walid Saad, Mehdi Bennis, Choong Seon Hong

Simulation results show that the error between policies of the optimal and the RNN-based controllers is less than 1. 5%.

A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning

3 code implementations18 Mar 2021 Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa, Mehdi Bennis

Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers.

Federated Learning

Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing

no code implementations15 Feb 2021 Christina Chaccour, Mehdi Naderi Soorki, Walid Saad, Mehdi Bennis, Petar Popovski, Merouane Debbah

Based on these fundamentals, we characterize seven unique defining features of THz wireless systems: 1) Quasi-opticality of the band, 2) THz-tailored wireless architectures, 3) Synergy with lower frequency bands, 4) Joint sensing and communication systems, 5) PHY-layer procedures, 6) Spectrum access techniques, and 7) Real-time network optimization.

Information Theory Information Theory

Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles

no code implementations5 Feb 2021 Tengchan Zeng, Omid Semiari, Mingzhe Chen, Walid Saad, Mehdi Bennis

The results also validate the feasibility of the contract-theoretic incentive mechanism and show that the proposed mechanism can improve the convergence speed of the DFP algorithm by 40% compared to the baselines.

Autonomous Vehicles Federated Learning

Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

no code implementations2 Feb 2021 Qianqian Zhang, Aidin Ferdowsi, Walid Saad, Mehdi Bennis

To guarantee an efficient learning process, necessary and sufficient conditions for the optimal UAV network topology that maximizes the learning rate for cooperative channel modeling are derived, and the optimal CGAN learning solution per UAV is subsequently characterized, based on the distributed network structure.

Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus

1 code implementation9 Jan 2021 Hang Chen, Syed Ali Asif, Jihong Park, Chien-Chung Shen, Mehdi Bennis

Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data.

Federated Learning

Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems

2 code implementations9 Jan 2021 Stefano Savazzi, Monica Nicoli, Mehdi Bennis, Sanaz Kianoush, Luca Barbieri

Next-generation autonomous and networked industrial systems (i. e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computing.

Federated Learning

Predictive Ultra-Reliable Communication: A Survival Analysis Perspective

no code implementations22 Dec 2020 Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Merouane Debbah

Results show that the accuracy of detecting channel blocking events is higher using the model-based method for low to moderate reliability targets requiring low sample complexity.

Survival Analysis Networking and Internet Architecture

Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning

no code implementations7 Dec 2020 Mohamed K. Abdel-Aziz, Cristina Perfecto, Sumudu Samarakoon, Mehdi Bennis, Walid Saad

Simulation results show the ability of the RL agents to efficiently learn the vehicles' association, RB allocation, and message content selection while maximizing vehicles' satisfaction in terms of the received sensory information.

reinforcement-learning

BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization

no code implementations9 Nov 2020 Tamara Alshammari, Sumudu Samarakoon, Anis Elgabli, Mehdi Bennis

This article deals with the problem of distributed machine learning, in which agents update their models based on their local datasets, and aggregate the updated models collaboratively and in a fully decentralized manner.

Federated Knowledge Distillation

1 code implementation4 Nov 2020 Hyowoon Seo, Jihong Park, Seungeun Oh, Mehdi Bennis, Seong-Lyun Kim

The goal of this chapter is to provide a deep understanding of FD while demonstrating its communication efficiency and applicability to a variety of tasks.

Federated Learning Knowledge Distillation

Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV

no code implementations3 Nov 2020 Qianqian Zhang, Walid Saad, Mehdi Bennis

In this paper, a novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed to enhance multi-user downlink transmissions over millimeter wave (mmWave) frequencies.

Distributional Reinforcement Learning reinforcement-learning

Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks

no code implementations20 Oct 2020 Ju-Hyung Lee, Jihong Park, Mehdi Bennis, Young-Chai Ko

Lastly, thanks to utilizing hybrid FSO/RF links, the proposed scheme achieves up to 62. 56x higher peak throughput and 21. 09x higher worst-case throughput than the cases utilizing either RF or FSO links, highlighting the importance of co-designing SAT-UAV associations, UAV trajectories, and hybrid FSO/RF links in beyond-5G NTNs.

Dimensionality Reduction reinforcement-learning

When Wireless Communications Meet Computer Vision in Beyond 5G

no code implementations13 Oct 2020 Takayuki Nishio, Yusuke Koda, Jihong Park, Mehdi Bennis, Klaus Doppler

This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications).

Image Reconstruction

Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces

no code implementations9 Oct 2020 George C. Alexandropoulos, Sumudu Samarakoon, Mehdi Bennis, Merouane Debbah

Reconfigurable Intelligent Surfaces (RISs) are recently gaining remarkable attention as a low-cost, hardware-efficient, and highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation.

Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels

no code implementations8 Oct 2020 Dingzhu Wen, Ki-Jun Jeon, Mehdi Bennis, Kaibin Huang

Targeting broadband channels, we consider the joint control of parameter allocation, sub-channel allocation, and transmission power to improve the performance of PARTEL.

Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM

no code implementations14 Sep 2020 Chaouki Ben Issaid, Anis Elgabli, Jihong Park, Mehdi Bennis, Mérouane Debbah

In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connected workers.

Quantization

Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems

no code implementations15 Jul 2020 Xianfu Chen, Celimuge Wu, Tao Chen, Zhi Liu, Honggang Zhang, Mehdi Bennis, Hang Liu, Yusheng Ji

Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics.

Edge-computing

Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning

no code implementations3 Jul 2020 Anis Elgabli, Jihong Park, Chaouki Ben Issaid, Mehdi Bennis

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker's model update while multiple workers' updates incur significant interference under limited bandwidth.

Federated Learning

Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup

no code implementations17 Jun 2020 Seungeun Oh, Jihong Park, Eunjeong Jeong, Hyesung Kim, Mehdi Bennis, Seong-Lyun Kim

This letter proposes a novel communication-efficient and privacy-preserving distributed machine learning framework, coined Mix2FLD.

Federated Learning

XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning

no code implementations9 Jun 2020 MyungJae Shin, Chihoon Hwang, Joongheon Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim

User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL).

Data Augmentation Federated Learning

Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning

no code implementations13 May 2020 Han Cha, Jihong Park, Hyesung Kim, Mehdi Bennis, Seong-Lyun Kim

Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent.

Data Augmentation reinforcement-learning

6G White Paper on Edge Intelligence

no code implementations30 Apr 2020 Ella Peltonen, Mehdi Bennis, Michele Capobianco, Merouane Debbah, Aaron Ding, Felipe Gil-Castiñeira, Marko Jurmu, Teemu Karvonen, Markus Kelanti, Adrian Kliks, Teemu Leppänen, Lauri Lovén, Tommi Mikkonen, Ashwin Rao, Sumudu Samarakoon, Kari Seppänen, Paweł Sroka, Sasu Tarkoma, Tingting Yang

We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers.

Edge-computing

Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation

no code implementations12 Mar 2020 Hamza Khan, M. Majid Butt, Sumudu Samarakoon, Philippe Sehier, Mehdi Bennis

Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G) and beyond wireless communication systems owing to higher spectrum efficiency, spatial gains, and energy efficiency.

Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning

no code implementations10 Mar 2020 Dingzhu Wen, Mehdi Bennis, Kaibin Huang

To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using data subsets.

Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory

no code implementations9 Mar 2020 Hamid Shiri, Jihong Park, Mehdi Bennis

Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions.

Federated Learning

Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning

no code implementations24 Feb 2020 Qianqian Zhang, Walid Saad, Mehdi Bennis

Furthermore, under limited knowledge of CSI, simulation results show that the proposed QR-DRL method, which learns a full distribution of the downlink rate, yields a better prediction accuracy and improves the downlink rate by 10% for online deployments, compared with a Q-learning baseline.

Distributional Reinforcement Learning Q-Learning +1

Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms

no code implementations19 Feb 2020 Tengchan Zeng, Omid Semiari, Mohammad Mozaffari, Mingzhe Chen, Walid Saad, Mehdi Bennis

Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition.

Federated Learning Trajectory Planning

Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation

no code implementations3 Feb 2020 Madhusanka Manimel Wadu, Sumudu Samarakoon, Mehdi Bennis

In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to a centralized training-based solution, under imperfect channel state information (CSI).

Networking and Internet Architecture

Cellular-Connected Wireless Virtual Reality: Requirements, Challenges, and Solutions

no code implementations13 Jan 2020 Fenghe Hu, Yansha Deng, Walid Saad, Mehdi Bennis, A. Hamid Aghvami

Cellular-connected wireless connectivity provides new opportunities for virtual reality(VR) to offer seamless user experience from anywhere at anytime.

Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach

no code implementations27 Nov 2019 Mohamed K. Abdel-Aziz, Sumudu Samarakoon, Mehdi Bennis, Walid Saad

Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles' AoI exceeds a predefined threshold and maximizing the knowledge about the network dynamics.

Active Learning GPR

L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning

no code implementations9 Nov 2019 Anis Elgabli, Jihong Park, Sabbir Ahmed, Mehdi Bennis

This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM).

Federated Learning

Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning

no code implementations23 Oct 2019 Anis Elgabli, Jihong Park, Amrit S. Bedi, Chaouki Ben Issaid, Mehdi Bennis, Vaneet Aggarwal

In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM).

Image Classification Quantization

Remote UAV Online Path Planning via Neural Network Based Opportunistic Control

no code implementations11 Oct 2019 Hamid Shiri, Jihong Park, Mehdi Bennis

This letter proposes a neural network (NN) aided remote unmanned aerial vehicle (UAV) online control algorithm, coined oHJB.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning

no code implementations30 Aug 2019 Anis Elgabli, Jihong Park, Amrit S. Bedi, Mehdi Bennis, Vaneet Aggarwal

When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an important problem and is the focus of this paper.

Distilling On-Device Intelligence at the Network Edge

no code implementations16 Aug 2019 Jihong Park, Shiqiang Wang, Anis Elgabli, Seungeun Oh, Eunjeong Jeong, Han Cha, Hyesung Kim, Seong-Lyun Kim, Mehdi Bennis

Devices at the edge of wireless networks are the last mile data sources for machine learning (ML).

Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

no code implementations6 Aug 2019 Xianfu Chen, Celimuge Wu, Tao Chen, Honggang Zhang, Zhi Liu, Yan Zhang, Mehdi Bennis

In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network.

Decision Making

Federated Reinforcement Distillation with Proxy Experience Memory

no code implementations15 Jul 2019 Han Cha, Jihong Park, Hyesung Kim, Seong-Lyun Kim, Mehdi Bennis

In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models.

reinforcement-learning

Multi-hop Federated Private Data Augmentation with Sample Compression

no code implementations15 Jul 2019 Eunjeong Jeong, Seungeun Oh, Jihong Park, Hyesung Kim, Mehdi Bennis, Seong-Lyun Kim

On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a central entity.

Data Augmentation

Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach

no code implementations10 May 2019 Hamid Shiri, Jihong Park, Mehdi Bennis

Afterwards, each UAV can control its acceleration by locally solving two partial differential equations (PDEs), known as the Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations.

Wireless Network Intelligence at the Edge

no code implementations7 Dec 2018 Jihong Park, Sumudu Samarakoon, Mehdi Bennis, Mérouane Debbah

), requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML).

Face Recognition Medical Diagnosis

Blockchained On-Device Federated Learning

2 code implementations12 Aug 2018 Hyesung Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim

By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified.

Information Theory Networking and Internet Architecture Information Theory

Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

no code implementations21 Jul 2018 Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Merouane Debbah

In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied.

Information Theory Information Theory

Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning

no code implementations16 May 2018 Xianfu Chen, Honggang Zhang, Celimuge Wu, Shiwen Mao, Yusheng Ji, Mehdi Bennis

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services.

Edge-computing reinforcement-learning

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

no code implementations11 May 2018 Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Merouane Debbah

It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79\% reductions in the amount of data that need to be exchanged.

Federated Learning

Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach

no code implementations7 May 2015 Meryem Simsek, Mehdi Bennis, Ismail Güvenc

The use of small cell deployments in heterogeneous network (HetNet) environments is expected to be a key feature of 4G networks and beyond, and essential for providing higher user throughput and cell-edge coverage.

Fairness PICO +1

Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks

no code implementations27 Aug 2013 Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Matti Latva-aho

In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed.

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