Privacy-Preserving Federated Learning using Flower Framework
Abstract
AI projects often face the challenge of limited access to meaningful amounts of training data. In traditional approaches, collecting data in a central location can be problematic, especially in industry settings with sensitive and distributed data. However, there is a solution -"moving the computation to the data" through Federated Learning. Federated Learning, a distributed machine learning approach, offers a promising solution by enabling model training across devices. It is a data minimization approach where direct access to data is not required. Furthermore, federated learning can be combined with techniques like differential privacy, secure aggregation, homomorphic encryption, and others, to further enhance privacy protection. In this hands-on tutorial, we delve into the realm of privacy-preserving machine learning using federated learning, leveraging the Flower framework which is specifically designed to simplify the process of building federated learning systems, as our primary tool. Moreover, we present the foundations of federated learning, explore how different techniques can enhance its privacy aspects, how it is being used in real-world settings today and a series of practical, hands-on code examples that showcase how you can federate any AI project with Flower, an open-source framework for all-this federated.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.