11 citations · 12 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…