104 citations · 305 across the 23 of their papers we have counts for
28 papers
AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator
Md. Kamrul Hossain, Walid Aljoby, Anis Elgabli +2
Federated Learning (FL) enables collaborative learning without exposing clients' data. While clients only share model updates with the aggregator, studies reveal that aggregators c…
Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning
Mohamed Badi, Chaouki Ben Issaid, Anis Elgabli +1
The growing number of wireless edge devices has magnified challenges concerning energy, bandwidth, latency, and data heterogeneity. These challenges have become bottlenecks for dis…
DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs
Chaouki Ben Issaid, Anis Elgabli, Mehdi Bennis
In this paper, we propose to solve a regularized distributionally robust learning problem in the decentralized setting, taking into account the data distribution shift. By adding a…
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi +3
Newton-type methods are popular in federated learning due to their fast convergence. Still, they suffer from two main issues, namely: low communication efficiency and low privacy d…
Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation
Mounssif Krouka, Anis Elgabli, Chaouki ben Issaid +1
Split-learning (SL) has recently gained popularity due to its inherent privacy-preserving capabilities and ability to enable collaborative inference for devices with limited comput…
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels
Mounssif Krouka, Anis Elgabli, Chaouki Ben Issaid +1
Today's intelligent applications can achieve high performance accuracy using machine learning (ML) techniques, such as deep neural networks (DNNs). Traditionally, in a remote DNN i…
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent
Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi +2
In this paper, we propose an energy-efficient federated meta-learning framework. The objective is to enable learning a meta-model that can be fine-tuned to a new task with a few nu…
Cross Layer Optimization and Distributed Reinforcement Learning for Wireless 360° Video Streaming
Anis Elgabli, Mohammed S. Elbamby, Cristina Perfecto +3
Wirelessly streaming high quality 360 degree videos is still a challenging problem. When there are many users watching different 360 degree videos and competing for the computing a…
BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization
Tamara Alshammari, Sumudu Samarakoon, Anis Elgabli +1
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 collaborati…
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
Chaouki Ben Issaid, Anis Elgabli, Jihong Park +2
In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connecte…
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Jihong Park, Sumudu Samarakoon, Anis Elgabli +4
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactive…
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
Anis Elgabli, Jihong Park, Chaouki Ben Issaid +1
Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs…
Extreme URLLC: Vision, Challenges, and Key Enablers
Jihong Park, Sumudu Samarakoon, Hamid Shiri +4
Notwithstanding the significant traction gained by ultra-reliable and low-latency communication (URLLC) in both academia and 3GPP standardization, fundamentals of URLLC remain elus…
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
Hamza Khan, Anis Elgabli, Sumudu Samarakoon +2
Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases. This paper investigates the problem of vehicle-cell association in millime…
L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning
Anis Elgabli, Jihong Park, Sabbir Ahmed +1
This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk, every worke…
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi +3
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). To reduce the number of communication li…
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi +2
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 importan…
Distilling On-Device Intelligence at the Network Edge
Jihong Park, Shiqiang Wang, Anis Elgabli +6
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML). As opposed to traditional ready-made public datasets, these user-generated privat…
A Proximal Jacobian ADMM Approach for Fast Massive MIMO Signal Detection in Low-Latency Communications
Anis Elgabli, Ali Elghariani, Vaneet Aggarwal +2
One of the 5G promises is to provide Ultra Reliable Low Latency Communications (URLLC) which targets an end to end communication latency that is less than 1ms . The very low latenc…
Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks
Anis Elgabli, Hamza Khan, Mounssif Krouka +1
Age of Information (AoI) measures the freshness of the information at a remote location. AoI reflects the time that is elapsed since the generation of the packet by a transmitter.…
GroupCast: Preference-Aware Cooperative Video Streaming with Scalable Video Coding
Anis Elgabli, Muhamad Felemban, Vaneet Aggarwal
In this paper, we propose a preference-aware cooperative video streaming system for videos encoded using Scalable Video Coding (SVC) where all the collaborating users are intereste…
A Low Complexity Detection Algorithm Based on Alternating Minimization
Anis Elgabli, Ali Elghariani, Vaneet Aggarwal +1
In this paper, we propose an algorithm based on the Alternating Minimization technique to solve the uplink massive MIMO detection problem. The proposed algorithm provides a lower c…
QoE-Aware Resource Allocation for Small Cells
Anis Elgabli, Ali Elghariani, Vaneet Aggarwal +1
In this paper, we study the problem of Quality of Experience (QoE) aware resource allocation in wireless systems. In particular, we consider application-aware joint Bandwidth-Power…
LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming
Anis Elgabli, Vaneet Aggarwal, Shuai Hao +2
Video streaming today accounts for up to 55\% of mobile traffic. In this paper, we explore streaming videos encoded using Scalable Video Coding scheme (SVC) over highly variable ba…