Privacy and Security Enhanced Federated Learning Framework Design
Abstract
Federated learning, while promising for its ability to train models across decentralized data sources, presents both security challenges and solutions.Its security depends on how well these challenges are addressed.On one hand, federated learning can be secure when proper privacy-preserving techniques are employed, such as encryption, secure aggregation, and diferential privacy, to protect data and model updates.Participant authentication, secure communication, and compliance with data protection regulations further contribute to its security.However, federated learning may also be vulnerable if not carefully implemented, potentially exposing sensitive data or risking model integrity.By encrypting and processing data using Crypten, federated learning participants can collaborate on model training without exposing their raw data, ensuring data privacy and confdentiality.Crypten's features, such as secure aggregation and encrypted computation, further bolster the trustworthiness of federated learning, making it an indispensable tool for applications in healthcare, fnance, and other sectors where sensitive data protection is paramount.In an era of growing data privacy concerns, Crypten empowers federated learning to thrive in a secure and privacy-preserving manner.In this project, we embarked on a comprehensive exploration of machine learning methodologies, starting with traditional plaintext machine learning techniques.Subsequently, we
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.