Mohoshin Ara
Tahera.

Graduate researcher building privacy-preserving federated learning systems for healthcare and intelligent transportation. I work at the intersection of trustworthy AI, system-level ML engineering, and proactive cyber defense at the AI CyberSafe Innovations Lab, University of Louisiana at Lafayette.

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Mohoshin Ara Tahera
About

A researcher in trustworthy systems.

I am a Graduate Research Assistant at the University of Louisiana at Lafayette, working in the AI CyberSafe Innovations Lab under the supervision of Dr. Shuvalaxmi Dass. My research sits at the intersection of privacy-preserving machine learning, federated systems, and proactive cyber defense, with applications in healthcare and intelligent transportation.

My published and under-review work systematizes the privacy landscape of federated fine-tuning for healthcare large language models — characterizing adversary types, attack surfaces, and layered defenses across the full LLM lifecycle. I have also developed a blockchain-secured federated transformer framework for real-time object detection in intelligent transportation systems, and I am designing network Moving Target Defense evaluation testbeds combining SDN, game theory, and adversarial modeling.

Before graduate school, I spent over four years as a Data Engineer at Grameenphone (Telenor Group), Bangladesh’s largest telecom operator serving 80M+ subscribers. That experience gave me a deep grounding in distributed data systems at production scale — infrastructure expertise that directly informs how I reason about federated learning deployments today.

Research Themes

Four threads of inquiry.

01 / Theme

Privacy-Preserving Federated Learning

Systematizing attack surfaces and layered defenses in federated fine-tuning of clinical LLMs and medical imaging systems.

02 / Theme

System-Level ML Engineering

Building federated pipelines that handle non-IID data, modality heterogeneity, edge latency, and Byzantine participants.

03 / Theme

Proactive Cyber Defense

SDN-based Moving Target Defense, game-theoretic attacker modeling, and adversarial evaluation frameworks.

04 / Theme

Applied Cryptography

Cryptographic primitives for trustworthy ML — group signatures, BFT consensus, and information-hiding schemes.

Research Output

Publications & manuscripts.

  1. [01]

    SoK: Privacy Threats, Defenses, and Challenges in Federated Fine-Tuning of Healthcare LLMs

    M. A. Tahera, S. Dass

    Accepted IEEE International Conference on Cyber Security & Resilience, 2026 First Author
  2. [02]

    SoK: Privacy-Aware LLMs in Healthcare — Threat Model, Privacy Techniques, Challenges, and Recommendations

    M. A. Tahera, K. S. Sidhu, S. Dass, S. Saha

    In Revision First Author
  3. [03]

    BlockSecRT-DETR: Secure and Efficient Federated Transformers for Real-Time Object Detection in ITS

    M. A. Tahera, S. Rahman, S. Dass, S. Ullah, M. Abouyoussef

    Accepted IEEE Vehicular Technology Conference, 2026 First Author
  4. [04]

    Federated Multimodal Bottleneck Transformer for Privacy-Preserving Ophthalmic Disease Detection

    M. A. Tahera, et al.

    In Preparation First Author
  5. [05]

    MTD-Playground: An Attacker-Aware Evaluation Framework for Network Moving Target Defense

    M. A. Tahera, et al.

    In Preparation Second Author
  6. [06]

    An Improved Data Hiding Technique in Image Steganography Using Genetic Algorithm

    M. A. Tahera, A. Das, S. Mondal

    Published Khulna University, 2018 First Author
Background

Research & industry experience.

Graduate Research Assistant

Feb 2025 — Present
AI CyberSafe Innovations Lab · University of Louisiana at Lafayette
Research
  • Authored a Systematization of Knowledge (SoK) paper on privacy threats in federated fine-tuning of healthcare LLMs, characterizing attack surfaces including gradient leakage, client update exposure, and communication interception across three pipeline stages.
  • Developed a comprehensive phase-aware SoK covering the full LLM lifecycle in healthcare, with detailed threat models mapping adversary capabilities to enabled attacks across data preprocessing, federated fine-tuning, and inference.
  • Designed BlockSecRT-DETR, a blockchain-secured federated RT-DETR framework for real-time ITS object detection with token engineering, round-scoped group signatures, and PBFT consensus for Byzantine-fault-tolerant aggregation.
  • Designing a federated multimodal learning framework for privacy-preserving ophthalmic disease detection across hospitals with heterogeneous imaging equipment — evaluated on Harvard-FairVision30k with demographic fairness analysis.
  • Building MTD-Playground, an enterprise-style SDN testbed (Containernet, Open vSwitch, ONOS) for benchmarking Moving Target Defense strategies under multi-stage attack campaigns.
  • Deploying distributed training infrastructure on the LONI HPC cluster (SLURM, PyTorch, CUDA) with custom data-loading pipelines for large-scale federated experiments.

Graduate Teaching Assistant — Software Methodology

Aug 2024 — Feb 2025
University of Louisiana at Lafayette
Teaching
  • Mentored 40+ undergraduates in software engineering projects, code review, debugging, and technical documentation.
  • Developed assessment rubrics and guided student research presentations on software design patterns and testing methodologies.

BI Developer & Data Engineer

Feb 2020 — Aug 2024
Grameenphone (Telenor Group) · Dhaka, Bangladesh · 80M+ subscribers · 4.5 years
Industry
  • Engineered distributed data pipelines on Oracle Exadata, Hadoop, and Spark for ML feature engineering over 50M+ subscriber records — infrastructure experience directly applicable to scaling federated learning systems.
  • Architected predictive analytics models for customer churn reduction and retailer stockout forecasting, informing enterprise-wide retention and supply chain strategy.
  • Reduced complex reporting overhead by 57% through end-to-end data automation and pipeline consolidation using SQL and Qlik Sense.
  • Developed in-house iOS and Android mobile applications (Swift, React Native) and real-time web dashboards (PHP, JavaScript) for executive decision-making.
  • Built customer segmentation tools for targeted micro-campaign generation across the 80M+ subscriber base, enabling data-driven marketing optimization.
  • Conducted R&D on data streaming architectures, big data tooling, and emerging BI technologies across distributed infrastructure.

Software Engineer

May 2018 — Feb 2020
Shiram System Solutions · Khulna, Bangladesh
Industry
  • Developed enterprise web applications using Laravel (PHP) for commercial and government clients, including internal systems for the Bangladesh Army.
Toolkit

Technical skills.

Languages

PythonJavaC/C++SQL BashPHPJavaScriptSwift

ML & AI

PyTorchTensorFlowScikit-learnHugging Face FlowerPySyftVision TransformersRT-DETR

Networking & Security

ONOSOpenFlowOpen vSwitchMininet ContainernetBlockchainNmapWireshark

Distributed Systems

HadoopSparkOracle ExadataAWS DockerSLURM/HPCLONIQlik Sense

Dev Tools

GitLinuxFlaskLaravel React NativeONNXLaTeXMATLAB
Portfolio

Selected projects.

⟡

FedMBT — Federated Multimodal Bottleneck Transformer

Federated learning framework for privacy-preserving ophthalmic disease detection across hospitals with heterogeneous imaging equipment. Evaluated on Harvard-FairVision30k with comprehensive demographic fairness analysis.

Federated LearningMultimodalMedical AIFairnessPyTorch
BlockSecRT-DETR

BlockSecRT-DETR — Blockchain-Secured Federated Detection

Federated RT-DETR framework with Token Engineering Module. Reduces encoder FLOPs by 47.8% and inference latency by 17.2%, with PBFT consensus and group-signature authentication across RSU nodes.

RT-DETRFederated LearningBlockchainKITTIPBFT
⬢

SoK — Privacy in Federated Fine-Tuning of Healthcare LLMs

Systematization of privacy risks across the federated training pipeline: client-side defenses (DP, SMPC, split learning), secure aggregation, and communication-layer strategies for clinical LLM deployments.

LLM PrivacyFederated LearningHealthcareDifferential Privacy
◈

SoK — Privacy-Aware LLMs in Healthcare (Full Lifecycle)

Phase-aware SoK covering data preprocessing, federated fine-tuning, and inference. Maps attack surfaces to defenses across adversary capability gradients and regulatory constraints.

LLMEHR/FHIRThreat ModelHIPAA/GDPR
⬡

MTD-Playground — Network Moving Target Defense Framework

Enterprise-style SDN testbed for benchmarking MTD strategies under multi-stage attack campaigns. Supports game-theoretic, AI/ML-driven, and SDN-based path randomization defense evaluation.

SDNONOSContainernetGame Theory
◊

Image Steganography Using Genetic Algorithm

GA-based steganographic system with cryptographic K-bit security key integration. Uses GA-driven chromosome selection for optimal embedding position with measurable PSNR/MSE improvements.

Genetic AlgorithmMATLABCryptographyPSNR/MSE