Jamal Bentahar

Colony: Robust Personalized ML With Contribution-Driven Connectivity and Resource Allocation in P2P Networks

Decentralized Personalized Learning (DPL) is an emerging paradigm that enables nodes to train personalized models across a peer-to-peer network without sharing their data or relying on a central server. However, nodes are often connected to a dynamic set of neighbors without prior knowledge of the quality of their data...

Hyperspherical Supervised Topic Models

Securing Vehicular Ad Hoc Networks via Digital Twin-Driven Anomaly Detection and Graph Reinforcement Learning

Resilient federated learning under data and system heterogeneity via genetic algorithm-based group client selection

A Genetic-Algorithm (GA) based client selection mechanism that is applicable to both horizontal FL (HFL) and vertical FL (VFL) and improves global model accuracy and accelerates convergence is proposed.

Deep reinforcement learning for autonomous and continual network intrusion detection