A Federated Learning (FL) Platform to Train the Machine Learning Model: A Step Towards Making FL More Efficient

Abstract

Federated learning is an emerging technology that can revolutionize the training of machine learning models. Federated learning refers to an approach to training a machine learning model in a decentralized and collaborative fashion. A central server distributes the model to client devices, where it is trained locally using the clients’ own data. The client then sends the updated model weights to the server, which aggregates them to update the global model. This paper introduces a federated learning platform designed to enable collaborative training of machine learning models across multiple client devices while preserving data privacy. The platform supports a range of supervised learning algorithms, including convolutional neural networks and decision trees, and is compatible with widely used frameworks such as TensorFlow, PyTorch, and Flower. It offers a user-friendly interface where model developers can upload or deploy their machine learning models to a central server. Clients can then access these models and train them locally using their own data. The platform's modular design ensures flexibility in deployment and efficiency in handling real-world applications. The key features of this application include a model repository, secure API access for client integration, local model training capabilities on user-end devices, and a user-friendly UI. The platform aims to democratize machine learning by enabling distributed model training and deployment, promoting collaboration and efficiency across diverse use cases. The scalable infrastructure supports real-time inference, on-device training, and secure data handling, making it ideal for industries ranging from healthcare to finance and beyond.