Federated Learning: A Comprehensive Guide

Introduction

Federated Learning (FL) is a decentralized approach to machine learning where models are trained across multiple devices or servers holding local data, without exchanging that data. This technique enhances privacy, reduces bandwidth consumption, and allows ML models to learn collaboratively while preserving data sovereignty.

1. Architecture of Federated Learning

The core architecture of Federated Learning consists of the following components:

a) Clients (Edge Devices)

These are devices such as smartphones, IoT sensors, or local servers that contain raw data. Each client trains the model locally on its dataset and only shares model updates (gradients) with the central server.

b) Federated Server (Aggregator)

The server acts as a central coordinator that collects updates from clients, aggregates them using techniques like FedAvg (Federated Averaging), and updates the global model before redistributing it to clients.

c) Communication Mechanism

Efficient communication protocols are used to transmit updates, reducing bandwidth consumption. Secure aggregation ensures privacy by…