Distributed AI

Table of Contents Introduction The Landscape of Federated Learning 2.1. Why Federated Learning Matters 2.2. The “Non‑IID” Problem Traditional Fixes and Their Limits Enter FederatedFactory 4.1. Core Idea: Swapping Generative Priors 4.2. One‑Shot Communication Explained 4.3. A Real‑World Analogy How FederatedFactory Works – Step by Step 5.1. Local Module Training 5.2. Central Aggregation of Generative Modules 5.3. Pseudo‑code Illustration Empirical Results: From Collapse to Near‑Centralized Performance 6.1. Medical Imaging Benchmarks (MedMNIST, ISIC2019) 6.2. CIFAR‑10 under Extreme Heterogeneity Why This Research Matters 7.1. Privacy‑First AI at Scale 7.2. Modular Unlearning – A Legal & Ethical Lever 7.3. Potential Real‑World Deployments Key Concepts to Remember Conclusion Resources Introduction Imagine a network of hospitals that each hold thousands of patient scans, but none of them can legally share raw images because of privacy regulations. They still want to train a powerful AI that can detect diseases across all their data. Federated Learning (FL) promises exactly that: a way to learn a shared model without moving the data off the local devices. ...

Introduction Over the past decade, large language models (LLMs) have reshaped how we interact with software, generate content, and automate decision‑making. Yet the sheer size of these models—often hundreds of billions of parameters—poses a fundamental dilemma for organizations that need low‑latency, privacy‑preserving, and cost‑effective AI at the edge. By 2026, the industry is witnessing a decisive shift toward localized small language models (SLMs) that run directly on private edge hardware, from industrial IoT gateways to consumer wearables. ...