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Found 5 papers, 0 papers with code
Subject Granular Differential Privacy in Federated Learning
We formally prove the subject level DP guarantee for our algorithms, and also show their effect on model utility loss.
Subject Membership Inference Attacks in Federated Learning
Using these attacks, we estimate subject membership inference risk on real-world data for single-party models as well as FL scenarios.
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions
Users can indirectly contribute to, and directly benefit from a much larger aggregate data corpus used to train the global model.
Report on the First HIPstIR Workshop on the Future of Information Retrieval
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR.
Private Federated Learning with Domain Adaptation
Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy.
