Exploring the potential of Collaborative Machine Learning on the Celo Blockchain: Enhancing Privacy and Collaboration with Federated Learning

In this tutorial, you will dive into the world of federated learning and explore how it can be implemented on the Celo blockchain to achieve privacy-preserving machine learning. Federated learning allows multiple participants to collaboratively train machine learning models without sharing their raw data, ensuring privacy protection while leveraging the collective intelligence of a decentralized network like the celo blockchain.

You will learn the principles and techniques of federated learning and understand how it can be applied to various machine learning scenarios. Through a step-by-step guide, you will develop a federated learning system on the Celo blockchain, enabling participants to train and improve machine learning models using their local data.

By utilizing the Celo blockchain’s decentralized infrastructure, you will ensure the integrity and transparency of the federated learning process. Smart contracts on the Celo blockchain will facilitate secure and auditable model updates and reward mechanisms for participants.

We shall also explore the integration of Chainlink oracle services to incorporate real-world data into the federated learning process, enabling the models to learn from diverse and up-to-date information while preserving privacy.

By the end of this tutorial, you will have a deep understanding of federated learning and its application to privacy-preserving machine learning on the Celo blockchain.


There are a couple of tutorials already written on oracle. You can do a quick search to see which of them can help you gain more insights into the topic. You can decide to be creative about it building one of the oracle’s use cases.