Federated learning is a decentralized machine learning approach that trains models locally on devices, sharing only model updates to preserve data privacy. Key advantages include enhanced data security, scalability, and reduced bandwidth usage, while challenges include communication overhead and system heterogeneity. Advanced trends like differential privacy integration and federated transfer learning are shaping its future, making it applicable in areas such as healthcare and IoT.

1. Federated Learning: Advanced
Trends
• Decentralized Machine Learning for Privacy
and Scalability

2. Introduction to Federated Learning
• What is Federated Learning?
• - A decentralized approach to training
machine learning models without centralizing
data.
• Core Concept:
• - Models are trained locally on devices; only
model updates are shared, not raw data.
• Applications:
• - Healthcare: Patient data privacy.

3. How Federated Learning Works
• Steps in Federated Learning:
• 1. Devices download the global model.
• 2. Train locally on their own data.
• 3. Share model updates (not raw data) with
the server.
• 4. Server aggregates updates to improve the
global model.
• Key Technologies:
• - Secure Aggregation, Differential Privacy.

4. Advantages of Federated Learning
• 1. Data Privacy:
• - No raw data leaves devices.
• 2. Scalability:
• - Trains on distributed devices or nodes.
• 3. Reduced Bandwidth:
• - Sharing updates instead of datasets reduces
data transfer costs.
• 4. Personalization:
• - Models can adapt to local data trends.

5. Challenges in Federated Learning
• 1. Communication Overhead:
• - Frequent updates can strain network
bandwidth.
• 2. System Heterogeneity:
• - Devices vary in computation power,
connectivity.
• 3. Data Heterogeneity:
• - Non-IID (non-independent, identically
distributed) data affects model performance.

6. Federated Learning Algorithms
• 1. Federated Averaging (FedAvg):
• - Aggregates local updates to form the global
model.
• 2. FedProx:
• - Addresses system heterogeneity by
regularizing local updates.
• 3. FedNova:
• - Normalized averaging to tackle imbalanced
updates.

7. Advanced Trends in Federated
Learning
• 1. Federated Learning with Differential
Privacy:
• - Ensures updates cannot reveal individual
data.
• 2. Federated Transfer Learning:
• - Enables collaboration across domains with
limited shared features.
• 3. Hierarchical Federated Learning:
• - Introduces intermediate aggregation layers
for efficiency.

8. Federated Learning and Edge AI
• Integration with Edge Computing:
• - Federated Learning complements edge
devices for real-time insights.
• Applications:
• - Autonomous vehicles, IoT devices, and
industrial systems.
• Future Trends:
• - Reduced latency, energy-efficient AI systems.

9. Real-World Use Cases
• 1. Healthcare:
• - Collaborative AI for diagnostics while
preserving patient privacy.
• 2. Mobile Devices:
• - Google’s Gboard: Federated Learning for
personalized suggestions.
• 3. Smart Grids:
• - Optimizing energy consumption across
distributed systems.

10. Future Directions and Conclusion
• Emerging Trends:
• - Integration with blockchain for secure
updates.
• - Advancements in communication-efficient
algorithms.
• Conclusion:
• - Federated Learning is reshaping AI by
balancing privacy, efficiency, and scalability.