Description

The Decentralised Mind – Part II is an advanced, research-driven guide to federated learning at scale. It explores optimization algorithms, convergence theory, privacy-preserving architectures, homomorphic encryption, adversarial robustness, and real-world deployment across healthcare, IoT, and edge systems. Bridging theory with practice, this book is essential for researchers, engineers, and graduate students building trustworthy, decentralized AI systems.

Key Highlights for Bullets:

Advanced FL Systems: Deep dive into optimization, convergence, and communication efficiency

Privacy & Security: Homomorphic encryption, differential privacy, adversarial robustness

Real-World Scale: Deployment challenges in healthcare, IoT, edge computing

Research Frontiers: One-shot FL, client drift correction, adaptive optimization

Audience: Researchers, engineers, graduate students in distributed AI