Federated Learning
Train Privacy-Preserving Machine Learning Models Across Distributed Edge Devices
In this course, you will: Understand federated learning architecture and the privacy problem it solves; Implement FedAvg, FedProx, and FedNova aggregation algorithms from scratch; Build federated learning systems using Flower, PySyft, and TensorFlow Federated.

30+
Hours
8
Modules
14
Topics
4.7
Rating
Beginner-Friendly
Level
New
Batches weekly
About Federated Learning
Train Privacy-Preserving Machine Learning Models Across Distributed Edge Devices
In this course, you will: Understand federated learning architecture and the privacy problem it solves; Implement FedAvg, FedProx, and FedNova aggregation algorithms from scratch; Build federated learning systems using Flower, PySyft, and TensorFlow Federated.
What This Training Covers
The Federated Learning programme at Tutorsbot spans 30+ hours across 8 structured modules. Every module is built around hands-on projects and real-world scenarios — not slide-heavy theory. Your instructor walks you through each concept with live demonstrations, code reviews, and practical exercises so you can apply what you learn from day one. The curriculum is aligned with current Technology Training industry expectations and hiring patterns.
Enrollment & Training Quality
Federated Learning is available in 2 flexible learning modes — choose online live classes, classroom, hybrid, self-paced, or one-on-one depending on your schedule. Every batch is limited in size to ensure each learner receives personal attention, code-level feedback, and doubt resolution. Career support and certification are included with every enrolment. Tutorsbot instructors are working professionals who teach from delivery experience, and the training standard stays consistent across all modes and batches.
Course Curriculum
8 modules · 14 topics · 30 hrs
01Federated Learning Fundamentals
7 topics
Federated Learning Fundamentals
7 topics
- Privacy problem in centralized training — Data sensitivity and regulation
- Federated learning concept — Model travels to data, not data to model
- Cross-device vs cross-silo federated learning — Scale and trust differences
- FL training loop — Local training, gradient upload, aggregation, distribution
- Communication efficiency — Rounds to convergence and bandwidth constraints
- Data heterogeneity — Non-IID distribution and its effect on convergence
- Federated learning ecosystem — Flower, PySyft, OpenFL, and TFF frameworks
02Aggregation Algorithms
7 topics
Aggregation Algorithms
7 topics
- FedAvg — Weighted averaging of client model weights
- FedProx — Proximal term regularization for heterogeneous data
- FedNova — Normalized gradient aggregation for unequal local updates
- SCAFFOLD — Control variates to reduce client drift in heterogeneous data
- FedBN — Batch normalization kept local to handle distribution shifts
- Personalized federated learning — Per-client adaptation with pFedMe and MAML
- Asynchronous aggregation — Handling stragglers and varying client speeds
Differential Privacy in Federated Learning
Topics included
5 more modules available
Enter your details to unlock the complete syllabus
Enrol in This Course
All prices inclusive of 18% GST. Same curriculum & certification across all formats. Updated Aug 2026.
Online Live
Live instructor-led sessions from anywhere, with recordings for catch-up.
GST ₹2,288 included
EMI from ₹2,500/mo
or
What You Get After Completion
Every graduate receives a verified certificate, a portfolio of real projects, and dedicated career support.
Verified Certificate
Digitally signed with a permanent shareable link — not just for attendance.
LinkedIn-importable·Permanent URL·PDF download
Project Portfolio
Real, deployable projects reviewed by your instructor — ready for interviews.
Instructor-reviewed·GitHub-hosted·Interview-ready
Career Support
Résumé review, mock interviews, LinkedIn guidance, and employer introductions.
1-on-1 coaching·Mock interviews·Employer connect
Meet Your Instructor
Every Federated Learning batch is led by a practitioner who teaches from production experience, not textbooks.
Industry Expert
Senior Technology Professional
Senior professionals with substantial hands-on delivery experience at top companies, bringing real-world projects, industry insights, and best practices.
How We Teach
- Concepts start with a real problem so theory lands in context
- Projects reviewed the way a senior colleague reviews pull requests
- Every topic includes the kind of questions you'll face in interviews
Hire Federated Learning Talent from Tutorsbot
Companies hiring Federated Learning talent from Tutorsbot receive pre-assessed profiles backed by project work, instructor review, and interview-ready candidates who can explain what they built and why.
Why hire from us
Project repositories with documented technical decisions
Assessment outcomes backed by instructor context
Candidate readiness shaped by interview-style practice
Project-based portfolios available
Frequently Asked Questions
Everything you need to know about Federated Learning, answered by our training experts
1Who should take Federated Learning?
2Does Federated Learning include a certificate?
3Is placement support included with Federated Learning?
4How long does Federated Learning take to complete?
5What is the mode of delivery for Federated Learning?
6Can I get a free demo class for Federated Learning?
7What kind of projects will I work on in Federated Learning?
8What if I miss a class?
9Is Federated Learning worth it for experienced professionals?
10What is the refund policy for Federated Learning?
11Do you offer corporate or group training?
12How are the instructors selected at Tutorsbot?
13Will I get lifetime access to Federated Learning materials?
14Can I switch between batch timings?
15What support do I get after completing the course?
Still have questions?
Technology Training