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This repository contains the course materials for the L361 Federated Learning: Theory and Practice course at the University of Cambridge.

简单的联邦学习算法实践

A horizontally federated learning demo in book Practicing Federated Learning Chapter 3.

Advancing Federated Learning in Practice: From Theory to Real-World Edge Applications

"SecureML-Model: A repository showcasing secure machine learning practices as part of my graduate studies. Focused on implementing adversarial robustness, data encryption, federated learning, and secure training methodologies to enhance the reliability and safety of ML systems."

This repository implements a simulated Federated Learning (FL) pipeline aligned with the Nexus Observatory Protocol (OP). It enables decentralized AI training on environmental data for flood and heatwave prediction, while integrating secure compute practices and mock zero-knowledge proofs to ensure verifiability.

The official repository of the Benchmarking Non-IID Data in Federated Graph Learning: A Systematic Review of Metrics, Protocols, and Evaluation Practices Paper

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Practicing federated learning--book code--python

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practice on "Practicing Federated Learning"

My federated learning practices

BITS research practice in federated learning with transformer models

Federated Learning Empowers Mobile IoT Devices: Practice and Algorithm Design

Concepts and practices of Federated Learning, a machine learning model developed by Google.

Federated learning infrastructure where practice becomes teaching. A stock exchange extracts. A practice exchange enriches.

This repository contains the course materials for the L361 Federated Learning: Theory and Practice course at the University of Cambridge.

This repo contains 4 lab sessions and the mini-project for Cambridge Computer Lab L361 - Federated Learning: Theory and Practice

The motivation of this project is to enhance agricultural productivity by offering data-driven solutions for crop selection, soil health, and pest management. By leveraging machine learning and federated learning, it provides personalized, privacy-preserving tools to help farmers make informed decisions and adopt sustainable farming practices

This project addresses the challenge of detecting multi-drug interactions (MDIs), which are a major source of adverse drug reactions in clinical practice. Using Federated Learning, the system predicts potential harmful interactions without requiring hospitals or pharma companies to share raw data.