Enhancing Privacy in Federated Learning: A Practical Assessment of Combined PETs in a Cross-Silo Setting
Abstract
Federated Learning (FL) has emerged as a revolutionary machine learning setting to enable collaborative training in a privacy-preserving way. However, recent research has showcased significant privacy attacks that pose a serious threat to the proliferation of FL as a technology designed to safeguard privacy during training with sensitive data from multiple entities. The rapid evolution of Privacy Enhancing Technologies offers promising methods for securing data inputs and outputs in FL scenarios. This paper evaluates and benchmarks the practical application of two PET methods, which has been integrated within a custom-built FL platform. The work conducts a comparative analysis of several privacy techniques applied to Federated Learning scenarios, with a primary focus on computational and communication performance.
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.