20 citations · 58 across the 27 of their papers we have counts for
29 papers
FLoKD: Adaptive Knowledge Distillation for Federated Low-Rank LLM over Wireless Networks
Xinlu Zhang, Na Yan, Yang Su +2
Large language models (LLMs) have demonstrated strong capabilities across a wide range of natural language processing tasks. However, conventional fine-tuning typically relies on c…
TT-Prune: Joint Model Pruning and Resource Allocation for Communication-efficient Time-triggered Federated Learning
Xinlu Zhang, Yansha Deng, Toktam Mahmoodi
Federated learning (FL) offers new opportunities in machine learning, particularly in addressing data privacy concerns. In contrast to conventional event-based federated learning,…
Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks
Xinlu Zhang, Na Yan, Yang Su +2
Federated learning (FL) for large language models (LLMs) offers a privacy-preserving scheme, enabling clients to collaboratively fine-tune locally deployed LLMs or smaller language…
xApp Conflict Mitigation with Scheduler
Idris Cinemre, Toktam Mahmoodi, Amirmohammad Farzaneh
Open RAN (O-RAN) fosters multi-vendor interoperability and data-driven control but simultaneously introduces the challenge of coordinating pre-trained xApps that may produce confli…
UltraFlwr -- An Efficient Federated Surgical Object Detection Framework
Yang Li, Soumya Snigdha Kundu, Maxence Boels +6
Surgical object detection in laparoscopic videos enables real-time instrument identification for workflow analysis and skills assessment, but training robust models such as You Onl…
AI-Native Multi-Access Future Networks -- The REASON Architecture
Konstantinos Katsaros, Ioannis Mavromatis, Kostantinos Antonakoglou +13
The development of the sixth generation of communication networks (6G) has been gaining momentum over the past years, with a target of being introduced by 2030. Several initiatives…
Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning
Xinlu Zhang, Yansha Deng, Toktam Mahmoodi
Time-triggered federated learning, in contrast to conventional event-based federated learning, organizes users into tiers based on fixed time intervals. However, this network still…
Direct-Conflict Resolution in Intent-Driven Autonomous Networks
Idris Cinmere, Kashif Mehmood, Katina Kralevska +1
As network systems evolve, there is an escalating demand for automated tools to facilitate efficient management and configuration. This paper explores conflict resolution in Intent…
Opportunistic Transmission of Distributed Learning Models in Mobile UAVs
Jingxin Li, Xiaolan Liu, Toktam Mahmoodi
In this paper, we propose an opportunistic scheme for the transmission of model updates from Federated Learning (FL) clients to the server, where clients are wireless mobile users.…
Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks
Deng Pan, Mohammad Ali Khoshkholghi, Toktam Mahmoodi
Unmanned aerial vehicles (UAV) or drones play many roles in a modern smart city such as the delivery of goods, mapping real-time road traffic and monitoring pollution. The ability…
A Novel Pre-Coding Based PAPR Reduction Scheme for IM/DD Systems
Idris Cinemre, Gokce Hacioglu
Orthogonal frequency division multiplexing (OFDM) is critical for high-speed visible light communication (VLC) transmission; however, it suffers from a high peak-to-average power r…
Dynamic Reliability: Reliably Sending Unreliable Data
Omar Nassef, Federico Chiariotti, Stephen Johnson +1
5G and Beyond networks promise low-latency support for applications that need to deliver mission-critical data with strict deadlines. However, innovations on the physical and mediu…
Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction
Jingxin Li, Toktam Mahmoodi, Hak-Keung Lam
Although federated learning has achieved many breakthroughs recently, the heterogeneous nature of the learning environment greatly limits its performance and hinders its real-world…
xURLCC in 6g with meshed RAN
Mohammad Ali Khoshkholghi, Toktam Mahmoodi, Subhankar Pal +3
5G Ultra-Reliable Low Latency Communications Technology (URLLC) will not be able to provide extremely reliable low latency services to the complex networks in 6G. Moreover, URLLC t…
Distributed Intelligence in Wireless Networks
Xiaolan Liu, Jiadong Yu, Yuanwei Liu +4
The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wire…
Slicing Scheduling for Supporting Critical Traffic in Beyond 5G
Ali Esmaeily, Katina Kralevska, Toktam Mahmoodi
One of the most challenging services fifth-generation (5G) mobile network is designed to support, is the critical services in-need of very low latency, and/or high reliability. It…
Experimental Demonstration of RAN Functional Split over virtual PON Transport Network
Line Larsen, Frank Slyne, Ghizlane Mountaser +2
Cloud-Radio Access Networks (Cloud-RANs) are separating the mobile networks base station functions into three units, the connection between the two of them is referred to as the fr…
The Role of Machine Learning for Trajectory Prediction in Cooperative Driving
Luis Sequeira, Toktam Mahmoodi
In this paper, we study the role that machine learning can play in cooperative driving. Given the increasing rate of connectivity in modern vehicles, and road infrastructure, coope…
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles
Omar Nassef, Luis Sequeira, Elias Salam +1
In this paper, a framework for lane merge coordination is presented utilising a centralised system, for connected vehicles. The delivery of trajectory recommendations to the connec…
A Lane Merge Coordination Model for a V2X Scenario
Luis Sequeira, Adam Szefer, Jamie Slome +1
Cooperative driving using connectivity services has been a promising avenue for autonomous vehicles, with the low latency and further reliability support provided by 5th Generation…
QUIC-EST: A Transmission Scheme to Maximize VoI of Multi-Stream Correlated Data Flows
Federico Chiariotti, Anay Ajit Deshpande, Marco Giordani +3
New advanced applications, such as autonomous driving and haptic communication, require to transmit multi-sensory data and require low latency and high reliability. These applicati…
Reconfigurable Computing Applied to Latency Reduction for the Tactile Internet
José C. V. S. Junior, Matheus F. Torquato, Toktam Mahmoodi +2
Tactile internet applications allow robotic devices to be remotely controlled over a communication medium with an unnoticeable time delay. In a bilateral communication, the accepta…
Latency Bounds of Packet-Based Fronthaul for Cloud-RAN with Functionality Split
Ghizlane Mountaser, Maliheh Mahlouji, Toktam Mahmoodi
The emerging Cloud-RAN architecture within the fifth generation (5G) of wireless networks plays a vital role in enabling higher flexibility and granularity. On the other hand, Clou…
Fixed-Mobile Convergence in the 5G era: From Hybrid Access to Converged Core
Massimo Condoluci, Stephen H. Johnson, Vicknesan Ayadurai +4
The availability of different paths to communicate to a user or device introduces several benefits, from boosting enduser performance to improving network utilization. Hybrid acces…