1 citations · 2 across the 11 of their papers we have counts for
12 papers
BackDFL: A Unified Benchmark For Backdoor Attacks and Defenses In Decentralized Federated Learning
Mouhamed Amine Bouchiha, Gregory Blanc, Yufei Han
Decentralized Federated Learning (DFL) promises trust-free collaborative learning by replacing the centralized parameter server with peer-to-peer model exchange. However, this arch…
Robust Reputation-Driven Crowdsourced Federated Learning
Mouhamed Amine Bouchiha, Gregory Blanc
Crowdsourced Federated Learning (CrowdFL) extends traditional federated learning by enabling open and heterogeneous participation through a crowdsourcing paradigm. In this setting,…
TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction
Mouhamed Amine Bouchiha, Gregory Blanc
Cyber Threat Intelligence (CTI) reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated analysis and reasoning. Cybersec…
DARTIC: Decentralized Anonymous Reputation at Scale for Trustworthy Crowdsourcing
Mouhamed Amine Bouchiha, Mourad Rabah, Ronan Champagnat +2
On-chain crowdsourcing leverages blockchain's decentralization, transparency, and tamper-resistance to build trustworthy and verifiable Web3 crowdsourced services. However, existin…
Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-Doudane
Federated learning (FL) has emerged as a promising paradigm for managing electric vehicle (EV) battery data in intelligent transportation systems (ITS), enabling privacy-preserving…
Towards Trustworthy Agentic IoEV: AI Agents for Explainable Cyberthreat Mitigation and State Analytics
Meryem Malak Dif, Mouhamed Amine Bouchiha, Abdelaziz Amara Korba +1
The Internet of Electric Vehicles (IoEV) envisions a tightly coupled ecosystem of electric vehicles (EVs), charging infrastructure, and grid services, yet it remains vulnerable to…
XAI-Driven Machine Learning System for Driving Style Recognition and Personalized Recommendations
Feriel Amel Sellal, Ahmed Ayoub Bellachia, Meryem Malak Dif +3
Artificial intelligence (AI) is increasingly used in the automotive industry for applications such as driving style classification, which aims to improve road safety, efficiency, a…
BotDetect: A Decentralized Federated Learning Framework for Detecting Financial Bots on the EVM Blockchains
Ahmed Mounsf Rafik Bendada, Abdelaziz Amara Korba, Mouhamed Amine Bouchiha +1
The rapid growth of decentralized finance (DeFi) has led to the widespread use of automated agents, or bots, within blockchain ecosystems like Ethereum, Binance Smart Chain, and So…
AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated Learning
Meryem Malak Dif, Mouhamed Amine Bouchiha, Mourad Rabah +1
Blockchained federated learning (BFL) combines the concepts of federated learning and blockchain technology to enhance privacy, security, and transparency in collaborative machine…
VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning
Ahmed Ayoub Bellachia, Mouhamed Amine Bouchiha, Yacine Ghamri-Doudane +1
Blockchain-based Federated Learning (FL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL f…
RollupTheCrowd: Leveraging ZkRollups for a Scalable and Privacy-Preserving Reputation-based Crowdsourcing Platform
Ahmed Mounsf Rafik Bendada, Mouhamed Amine Bouchiha, Mourad Rabah +1
Current blockchain-based reputation solutions for crowdsourcing fail to tackle the challenge of ensuring both efficiency and privacy without compromising the scalability of the blo…
LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models
Mouhamed Amine Bouchiha, Quentin Telnoff, Souhail Bakkali +4
Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable perf…