🚀 Welcome to my journey for the #30DaysOfFLCode challenge!
Commit to 30 days of learning, sharing, and building Federated Learning, and other Privacy Enhancing Technologies (PETs) skills.
📄 Details of the challenge could be found at OpenMined website.
Here is my learning journey:
- Day 1️⃣ 1. Introduction
- Day 2️⃣ 2.1 Communication Efficiency
- Day 3️⃣ 2.2 Systems Heterogeneity
- Day 4️⃣ 2.3 Statistical Heterogeneity
- Day 5️⃣ 2.4 Privacy
- Day 3️⃣0️⃣ Federated Learning in Practice: Reflections and Projections
- Day 6️⃣ Introduction and pseudo-code
- Day 7️⃣ Strengths, limitations, key experiment findings, and future directions
- Day 1️⃣1️⃣ Hands-on: Replicate Experiment pt.1
- Day 1️⃣2️⃣ Hands-on: Replicate Experiment pt.2
- Day 8️⃣ What is it, How to use it, Key Features, General Considerations
- Day 9️⃣ Structure and How to Install APIs
- Day 🔟 Example of usage with 'RingApp'
- Day 2️⃣2️⃣ Federated Browser History Analyser System
- Day 2️⃣3️⃣ Start being part of Computational Model
- Day 2️⃣4️⃣ "Show & Tell": Federated Browser History Analyser
- Day 2️⃣6️⃣ Federated CPU Tracker
- Day 1️⃣3️⃣ Privacy Principles for Learning and Analytics
- Day 1️⃣4️⃣ Federated Learning Settings and Applications: Cross-Device Federated Learning
- Day 1️⃣5️⃣ Privacy for Federated Computations and Data Minimization for Aggregation
- Day 1️⃣6️⃣ Federated Analytics
- Day 1️⃣7️⃣ Introduction and Background
- Day 1️⃣8️⃣ Federated Optimization Methods
- Day 1️⃣9️⃣ Convergence Analysis
- Day 2️⃣0️⃣ Experiment Results
- Day 2️⃣7️⃣ The CaPC control
- Day 2️⃣8️⃣ Privacy-Preserving RAG with DP
- Day 2️⃣9️⃣ RAPPOR
👩🔬 Author: Giulia Gualtieri
📧 Email: giulia.gualtieri@mail.polimi.it
🗺️ Location: Switzerland