##Introduction This repository contains the implementation of Federated Learning for vision models.

Federated Learning is a decentralized machine learning approach where models are trained collaboratively across multiple devices while keeping data localized. This ensures privacy and security of data while leveraging the computational power of edge devices.

##Features

• Decentralized Training: Train models across multiple devices without sharing data.
• Privacy Preserving: Data remains on the local device, enhancing privacy and security.
• Scalable: Easily scale the training process to a large number of devices.
• Custom Vision Models: Support for various vision model architectures including CNNs, ResNet, etc.
• Extensive Logging and Monitoring: Keep track of training progress and performance metrics.

##Installation Prerequisites Python 3.7+