Private Federated Learning with Domain Adaptation

Authors: Daniel Peterson, Pallika Kanani, Virendra J. Marathe
Published: 2019-12-13

Source: https://arxiv.org/abs/1912.06733

PDF: https://arxiv.org/pdf/1912.06733

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プライバシー保護 機械学習技術 性能評価指標

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Abstract

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. We propose a framework to augment this collaborative model-building with per-user domain adaptation. We show that this technique improves model accuracy for all users, using both real and synthetic data, and that this improvement is much more pronounced when differential privacy bounds are imposed on the FL model.