01
Adaptive layer-wise quantization
4–8 bit integer precision per layer, chosen dynamically from gradient-norm history — replacing FedSparQ's fixed FP16 scheme with something that actually responds to how much each layer is changing.
M.Tech · IIT Jodhpur . Researcher
Compressing intelligence
to fit on the edge.
I'm Tabrej Alam — a federated learning researcher building communication-efficient training systems for resource-constrained devices. My current work, EF‑AdapSparseQ, pushes adaptive quantization and error-feedback past fixed-precision baselines under heavy non‑IID conditions.
8.92
CGPA · M.Tech SIOT
7
FL methods benchmarked
0.021
EF buffer norm @ round 15
live render — client ↔ server gradient exchange
scroll
// 01 — about
Background & trajectory
I'm currently pursuing my M.Tech in Electrical Engineering (Sensors & IoT) at the Indian Institute of Technology, Jodhpur, ranked 2nd in the ongoing batch. My thesis sits at the intersection of federated learning and model compression — specifically, how to quantize and sparsify gradient updates so that edge devices with limited bandwidth and compute can still train collaboratively without leaking raw data or collapsing under non‑IID conditions.
I am also to design and build production-grade, AI-native platforms. Looking to leverage expertise in Python, full-stack architectures, and GenAI integration to develop scalable, reusable enterprise solutions and intelligent agents.
Before this, I completed my B.Tech in Computer Science & Engineering at HNB Garhwal University, where I built full‑stack web applications and explored applied machine learning — a foundation that now shows up in how I engineer FL simulation pipelines rather than just theorize about them.I'm preparing my current results — EF‑AdapSparseQ — for presentation to my thesis supervisor and for submission to IEEE Access, framed around accuracy‑per‑bit gains under severe non‑IID data splits (α = 0.1).
2025 —
M.Tech (SIOT), IIT Jodhpur · CGPA 8.92 · Rank 2
2025 —
Qualified GATE
2021–25 —
B.Tech CSE, HNB Garhwal University · CGPA 7.67
2020 —
Class XII, Jeevan Public School, Motihari
2018 —
Class X, St. Michael's Academy, Bettiah
// 02 — research
Federated learning, compressed
My thesis proposes EF‑AdapSparseQ, extending FedSparQ with three concrete innovations aimed at communication-efficient, robust FL on edge hardware.
02
Gradient-norm bit scheduling
A coarse‑to‑fine bit-width schedule driven by gradient-norm trends, so early noisy rounds spend fewer bits and later, more meaningful updates get more precision.
03
Per-client error-feedback buffers
Each client retains its own EF buffer to carry forward quantization error, preventing gradient drift under heavy non‑IID partitioning.
Validated result
Error-feedback buffer norm converges near zero (~0.021 at round 15) versus FedSparQ's persistent oscillation (~0.23) — empirically supporting the convergence theorem behind the method.
- LeNet‑5 on CIFAR‑10
- Dirichlet non‑IID, α = 0.1 / 0.5 / 1.0
- vs. FedAvg, QSGD, Top‑K, FedPAQ, FedProx, FedSparQ
// 03 — build log
Things I've shipped
Motor speed control via computer vision
Real-time gesture and color-based motor control. Hand detection at 120ms, color detection at 85ms — 95% gesture accuracy, 92% color-detection accuracy.
PythonOpenCVReal-time CV
Federated Learning / Distributed AI
Frame your Federated Learning research as an enterprise-grade, communication-efficient distributed AI system. Emphasize optimization, latency reduction, and privacy-preserving model aggregation.
PythonCIFAR10Best Acc/Mb · Lowest conv. comm · Theorem 1
E-mail spam detection system
Classical ML pipeline using Python and Scikit-learn, reaching 92% classification accuracy on spam vs. legitimate mail.
PythonScikit-learnNLP
Event management system
Web application for managing events, bookings and schedules end‑to‑end, improving event scheduling efficiency by roughly 40%.
HTML/CSSJavaScriptMySQL
GUI scientific calculator
A desktop scientific calculator built with a Python GUI toolkit — clean operator precedence handling and a no-frills interface.
PythonTkinter
// 04 — stack
Tooling that compiles, trains, and ships
Languages
CC++Python
JavaVerilogSystemVerilog
AI & GenAI Frameworks
LangChain LangGraph LlamaIndex
OpenAI/Claude APIs
Web
HTMLCSSJavaScript
React.jsNode.jsExpress.js
Data
MySQLMongoDB
Tools & platforms
GitGitHubVS Code
VivadoEDA PlaygroundKeil
MATLABGoogle ColabPyTorch
research interests —
Federated Learning for IoT
/
Timing Analysis of RTL
// 05 — signal
Achievements
2nd
Currently ranked 2nd in the ongoing M.Tech SIOT programme at IIT Jodhpur
✓
Qualified GATE 2025
⟁
Graph Theory Programming Camp Certificate — AlgoUniversity
// 06 — connect
Let's exchange gradients.
Let's exchange gradients.
(or just talk research.)
Open to discussions on federated learning, edge AI, model compression, or full-stack collaboration. Reach out directly — no intermediary server required.
email
m25eet009@iitj.ac.in
phone
+91 6204 765522
linkedin
/in/tabrej-alam0178
current location
IIT Jodhpur, Rajasthan, India