Federated Machine Learning - The-Learners-Community/RoadMaps-and-Resources GitHub Wiki

Federated Machine Learning (Federated ML) Roadmap

A comprehensive roadmap and course outline for mastering Federated Machine Learning (Federated ML), progressing from beginner to advanced mastery. Each stage includes structured topics and relevant projects to deepen your understanding practically and theoretically.

1. Introduction to Federated Learning (Beginner)

Core Concepts

  • What is Federated Learning (FL)?
  • Centralized vs. Decentralized vs. Federated Learning
  • Privacy-preserving motivations for FL
  • Common terminology and frameworks (TensorFlow Federated, PySyft, Flower)

Projects

  • Federated Hello World: Set up a basic federated learning environment using TensorFlow Federated and run a simple MNIST digit classifier.
  • Simulation of Federated Data Distribution: Generate synthetic data partitions to understand non-i.i.d. distributions and their effects on model accuracy.

2. Fundamentals of Machine Learning and Statistics for FL (Beginner to Intermediate)

Core Concepts

  • Supervised and unsupervised learning overview
  • Neural networks fundamentals
  • Basic statistical principles: distributions, sampling methods, variance
  • Privacy considerations: Differential privacy basics

Projects

  • Federated Logistic Regression: Implement logistic regression under federated settings to classify binary data.
  • Privacy Evaluation: Measure data leakage potential by analyzing gradients exchanged in federated setups.

3. Intermediate Federated Learning Techniques (Intermediate)

Core Concepts

  • Federated Averaging (FedAvg) algorithm in-depth
  • Communication-efficient algorithms (FedProx, FedMA)
  • Federated optimization methods: Stochastic Gradient Descent (SGD), Adam in FL context
  • Handling non-i.i.d. data challenges

Projects

  • Comparative Study of Federated Optimizers: Experiment with FedAvg, FedProx, and FedMA algorithms and analyze performance differences.
  • Image Classification with Federated CNNs: Use CIFAR-10 dataset across simulated federated devices to train a CNN model.

4. Privacy and Security in Federated Learning (Intermediate to Advanced)

Core Concepts

  • Advanced Differential Privacy Techniques
  • Secure Aggregation Protocols
  • Threat modeling in federated environments
  • Adversarial attacks on Federated ML models

Projects

  • Implementing Differential Privacy (DP): Integrate DP with FedAvg to evaluate trade-offs between privacy and performance.
  • Federated Poisoning Attack Simulation: Create a controlled scenario to observe effects of malicious participants on model training.

5. Advanced Federated Learning Algorithms (Advanced)

Core Concepts

  • Personalized Federated Learning (Per-FedAvg, FedPer, pFedMe)
  • Federated transfer learning and federated meta-learning
  • Adaptive communication strategies: compression and quantization techniques
  • Hierarchical federated learning

Projects

  • Personalized Federated Recommender System: Build a personalized movie recommendation system using federated meta-learning methods.
  • Federated Transfer Learning: Apply federated transfer learning to domain adaptation problems (e.g., medical imaging or text classification).

6. Federated Learning at Scale (Advanced)

Core Concepts

  • Infrastructure and orchestration (Kubernetes, cloud integration)
  • Distributed communication protocols (gRPC, WebSocket)
  • Handling scalability challenges: latency, bandwidth constraints, asynchronous FL
  • Deployment strategies for federated models (Edge deployment)

Projects

  • Federated Learning Deployment with Flower and Kubernetes: Set up a scalable federated learning experiment using Kubernetes orchestration and Flower.
  • Asynchronous Federated Learning: Implement and evaluate asynchronous federated averaging on large-scale simulations.

7. Emerging Topics and Research Directions (Expert/Mastery)

Core Concepts

  • Federated Reinforcement Learning
  • Cross-device vs. Cross-silo federated learning scenarios
  • Vertical Federated Learning: Multi-party computation for FL
  • Advanced theoretical foundations and convergence analysis
  • Decentralized (peer-to-peer) Federated Learning

Projects

  • Federated Reinforcement Learning (FedRL): Develop an environment using federated reinforcement learning.
  • Vertical Federated Learning Implementation: Demonstrate vertical federated learning with multi-party computation (MPC) for cross-silo data integration.

8. Real-world Federated Learning Applications (Mastery/Expert)

Core Concepts

  • Healthcare: Federated medical image analysis, electronic health record modeling
  • Finance: Federated fraud detection, credit scoring
  • Edge Devices and IoT: Mobile keyboards, speech recognition, federated analytics
  • Ethical considerations, governance, and compliance (GDPR, HIPAA)

Projects

  • Federated Learning for Healthcare: Build a federated learning pipeline for disease prediction from electronic health records.
  • Federated Fraud Detection in Finance: Develop federated models for detecting transaction anomalies across decentralized banks.

9. Research and Contribution (Mastery/Expert)

Core Concepts

  • Identifying open research problems in federated learning
  • Writing and reviewing research papers
  • Contributing to open-source federated learning libraries (PySyft, TensorFlow Federated, Flower)
  • Publishing research, attending conferences (NeurIPS, ICML, FL-specific workshops)

Projects

  • Original Research Project: Conduct novel research in improving convergence speed, privacy trade-offs, or communication efficiency in federated learning.
  • Open Source Contribution: Contribute to existing open-source federated learning frameworks, enhancing documentation, scalability, or security.

📅 Suggested Weekly Rhythm

  1. Read one research paper or doc section
  2. Code its core idea
  3. Write a brief blog post explaining what you learned
  4. Demo your project to the community for feedback