57 citations · 78 across the 5 of their papers we have counts for
5 papers
Subject Granular Differential Privacy in Federated Learning
Virendra J. Marathe, Pallika Kanani, Daniel W. Peterson +1
This paper considers subject level privacy in the FL setting, where a subject is an individual whose private information is embodied by several data items either confined within a…
Subject Membership Inference Attacks in Federated Learning
Anshuman Suri, Pallika Kanani, Virendra J. Marathe +1
Privacy attacks on Machine Learning (ML) models often focus on inferring the existence of particular data points in the training data. However, what the adversary really wants to k…
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions
Pallika Kanani, Virendra J. Marathe, Daniel Peterson +2
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 indirect…
Report on the First HIPstIR Workshop on the Future of Information Retrieval
Laura Dietz, Bhaskar Mitra, Jeremy Pickens +21
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. The first ite…
Private Federated Learning with Domain Adaptation
Daniel Peterson, Pallika Kanani, Virendra J. Marathe
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 p…