mlsanityData to 14 Oct 2026

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

arXiv 2103.07491 ↗First posted 12 Mar 2021cs.LG cs.AI cs.CRcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 64 references that are in the map. First posted on arXiv 12 Mar 2021.

Builds on

32 of 64 references.

References · 32

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20 more
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Citing papers per year: 2022: 1.

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  1. 2022On Privacy and Personalization in Cross-Silo Federated Learningcited by 36