Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions

TL;DR

This paper explores applying federated learning to extract vaccine adverse event mentions, analyzing privacy constraints, personalization techniques, and their impact on model accuracy in sensitive health data scenarios.

Contribution

It provides a comprehensive empirical analysis of federated learning for NER in vaccine adverse event detection, including privacy challenges and personalization solutions.

Findings

Local differential privacy can reduce model accuracy significantly.

Personalization methods like FedFT help recover lost accuracy.

FedFT is not PAC Identifiable, enhancing privacy.

Abstract

Federated Learning (FL) is quickly becoming a goto distributed training paradigm for users to jointly train a global model without physically sharing their data. Users can indirectly contribute to, and directly benefit from a much larger aggregate data corpus used to train the global model. However, literature on successful application of FL in real-world problem settings is somewhat sparse. In this paper, we describe our experience applying a FL based solution to the Named Entity Recognition (NER) task for an adverse event detection application in the context of mass scale vaccination programs. We present a comprehensive empirical analysis of various dimensions of benefits gained with FL based training. Furthermore, we investigate effects of tighter Differential Privacy (DP) constraints in highly sensitive settings where federation users must enforce Local DP to ensure strict privacy guarantees. We show that local DP can severely cripple the global model's prediction accuracy, thus dis-incentivizing users from participating in the federation. In response, we demonstrate how recent innovation on personalization methods can help significantly recover the lost accuracy. We focus our analysis on the Federated Fine-Tuning algorithm, FedFT, and prove that it is not PAC Identifiable, thus making it even more attractive for FL-based training.