Home Blog Learn

All Courses

Advanced Federated Learning Techniques

Chapter 1: Federated Learning Foundations Revisited

Federated Learning Principles: A Recap

Challenges in Federated Environments

Mathematical Formulation of Federated Optimization

Synchronous vs. Asynchronous Federated Learning Models

Threat Models in Federated Learning

Evaluating Federated Learning Systems

Chapter 2: Advanced Aggregation Algorithms

Limitations of Federated Averaging (FedAvg)

FedProx: Addressing Statistical Heterogeneity

SCAFFOLD: Variance Reduction in Federated Optimization

FedNova: Normalized Averaging for Heterogeneous Systems

Byzantine-Robust Aggregation Methods

Adaptive Federated Optimization Techniques

Practice: Implementing Advanced Aggregation

Chapter 3: Enhancing Privacy in Federated Learning

Differential Privacy Mechanisms for FL

Applying DP to Gradient Updates

Composition Theorems and Privacy Budget Management

Secure Multi-Party Computation (SMC) Protocols for Aggregation

Homomorphic Encryption (HE) for Secure Aggregation

Comparing DP, SMC, and HE in FL Contexts

Privacy Attacks: Inference and Reconstruction

Hands-on Practical: Implementing DP-FedAvg

Chapter 4: Addressing Heterogeneity and Personalization

Sources of Heterogeneity: Statistical and System

Techniques for Handling Non-IID Data Distributions

Clustered Federated Learning Approaches

Meta-Learning for Federated Personalization

Multi-Task Learning in Federated Settings

Model Pruning and Adaptation for Device Constraints

Practice: Simulating Non-IID Data and Mitigation

Chapter 5: Communication Efficiency and System Optimization

Communication Bottlenecks in Federated Learning

Gradient Compression Techniques

Error Accumulation and Compensation Methods

Model Update Compression Strategies

Optimizing Local Computation

Asynchronous Federated Learning Optimizations

Hands-on Practical: Implementing Gradient Quantization

Chapter 6: Federated Learning System Design and Implementation

Architecture of Federated Learning Systems

Overview of FL Frameworks

Simulation vs. Real-World Deployment Considerations

Monitoring and Debugging Federated Systems

Cross-Silo vs. Cross-Device FL Implementations

Security Considerations in System Deployment

Practice: Setting up a Basic FL Simulation

Advanced Federated Learning Techniques

Prerequisites: Strong foundation in machine learning, proficiency in Python and deep learning frameworks (TensorFlow/PyTorch), familiarity with distributed systems concepts. Basic understanding of cryptography recommended.

Level: Advanced

What You'll Learn

Advanced Aggregation
Implement and analyze aggregation algorithms beyond FedAvg, addressing issues like heterogeneity and Byzantine clients.
Privacy Enhancement
Apply differential privacy, secure multi-party computation, and homomorphic encryption techniques within federated learning.
Heterogeneity Management
Develop and evaluate strategies for handling statistical (Non-IID data) and systems heterogeneity across clients.
Personalization Techniques
Implement personalization methods in FL, including meta-learning and clustered approaches.
Communication Optimization
Apply gradient compression, sparsification, and quantization techniques to improve communication efficiency.
System Design
Design, implement, and analyze federated learning systems using established frameworks and considering security implications.

© 2025 ApX Machine Learning

;