~/Research

Research &
Education

Advancing the frontiers of Byzantine-robust federated learning and autonomous systems through rigorous academic research

ACADEMIC WORK 02 PUBLICATIONS 03 ACADEMIC ENTRIES 04 FIELDS
§ 01 Publications 02 ENTRIES

Research Output

Peer-reviewed publications in leading conferences and journals

[01] 2025 · Journal Under Review

A3: Adaptive Attack-Aware Aggregation for Byzantine-Robust Federated Learning

H. Tariq Butt, S. Khan, K.F.A. Khan, I. Munir, S. Nazir

Springer Information Systems Frontiers

Novel Byzantine-resilient aggregation technique for federated learning achieving 94.99% average accuracy with only 0.71% variance across 5 attack types - significantly outperforming state-of-the-art methods (TrimmedMean: 2.62% variance, Multikrum: 54% variance). Features triple-weighted aggregation mechanism (diversity, confidence, trust) with adaptive strategy selection. Validated on CIC-IDS2017 dataset (2.8M network flows) with comprehensive statistical analysis.

Federated LearningByzantine RobustnessNetwork SecurityIntrusion Detection
[02] 2025 · Journal Under Review

The Practitioner's Dilemma: A Critical Review of Trade-offs between Privacy, Efficiency, and Model Utility in Federated Learning Systems

H. Tariq Butt, S. Khan, I. Munir, K.F.A. Khan, S. Nazir

Springer Computing

Comprehensive PRISMA-style systematic review quantifying the fundamental trilemma in federated learning: privacy, efficiency, and utility cannot be simultaneously maximized. Meta-analysis of 100+ papers and production systems (2022-2025). Key findings: Strong differential privacy (ε<1) causes 5-15% accuracy loss; Cryptographic security has 2-5× overhead; Communication optimization achieves 90%+ bandwidth reduction. Introduces five-dimensional assessment framework for Pareto-optimal FL designs.

Federated LearningPrivacySystematic ReviewTrade-off Analysis
§ 02 Academic Background 03 ENTRIES

Education

Sep 2024 - Oct 2025 Part-time · Hybrid

Graduate Research Assistant

Federated Learning & Privacy-Preserving ML

National University of Sciences and Technology (NUST)

Islamabad, Pakistan · Sep 2024 - Oct 2025

Highlights

  • Designed A3 aggregation algorithm achieving 94.99% accuracy with only 0.71% variance across 5 Byzantine attack types
  • Outperformed state-of-the-art methods: TrimmedMean (2.62% variance), Multikrum (54% variance)
  • Produced two research papers submitted to peer-reviewed venues (Springer journals, Under Review)
  • Conducted systematic review analyzing 100+ federated learning papers (2022-2025)
  • Quantified privacy-efficiency-utility trilemma in production FL systems
  • Validated on CIC-IDS2017 dataset with 2.8M network flow records
2023 - 2025

Master of Science

Robotics & Artificial Intelligence

National University of Sciences and Technology (NUST)

Islamabad, Pakistan · 2023 - 2025

CGPA 3.7/4.0
Thesis:

A3: Adaptive Attack-Aware Aggregation for Byzantine-Robust Federated Learning

Highlights

  • Developed novel Byzantine-resilient federated learning algorithm achieving state-of-the-art performance (94.99% accuracy, 0.71% variance across 5 attack types)
  • Two research papers submitted to peer-reviewed venues (Under Review, 2025)
  • Conducted comprehensive systematic review analyzing 100+ federated learning papers
  • Research directly applied to production security systems at Cybersilo
  • Supervisor: Dr. Shahbaz Khan
2018 - 2022

Bachelor of Science

Computer Science

Arid Agriculture University

Rawalpindi, Pakistan · 2018 - 2022

CGPA 3.96/4.0
Award Gold Medal - Highest CGPA in Graduating Class

Highlights

  • First Class with Distinction
  • Gold Medal for achieving highest CGPA in graduating class
  • Comprehensive foundation in software engineering, algorithms, and cybersecurity
  • Consistent academic excellence throughout the program with hands-on project experience
§ 03 Interests 04 FIELDS

Research Interests

My research focuses on making distributed machine learning systems robust, secure, and efficient. I'm particularly interested in Byzantine-robust algorithms, federated learning, and their applications in autonomous systems and edge computing environments.

01

Federated Learning

02

Byzantine Robustness

03

Autonomous Systems

04

Edge Computing