Private Federated Learning with Domain Adaptation
arXiv:1912.06733
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.
Presented at the Workshop on Federated Learning for Data Privacy and Confidentiality (in Conjunction with NeurIPS 2019)
References in corpus (9)
- Deep Learning with Differential Privacy
- Federated Optimization: Distributed Machine Learning for On-Device Intelligence
- Frustratingly Easy Domain Adaptation
- Towards Federated Learning at Scale: System Design
- Domain Adaptation for Statistical Classifiers
- Secure Federated Transfer Learning
- Federated Optimization:Distributed Optimization Beyond the Datacenter
- Differentially Private Federated Learning: A Client Level Perspective
- Differential Private Stack Generalization with an Application to Diabetes Prediction
Cited by in corpus (21)
- Three Approaches for Personalization with Applications to Federated Learning
- Federated Learning of a Mixture of Global and Local Models
- Memory-aware curriculum federated learning for breast cancer classification
- Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning
- Salvaging Federated Learning by Local Adaptation
- Lower Bounds and Optimal Algorithms for Personalized Federated Learning
- Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
- Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
- Personalized Federated Learning with First Order Model Optimization
- A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
- FedJAX: Federated learning simulation with JAX
- Federated Residual Learning
- Specialized federated learning using a mixture of experts
- Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
- Voting-based Approaches For Differentially Private Federated Learning
- Practical and Secure Federated Recommendation with Personalized Masks
- Addressing Class Imbalance in Federated Learning
- Personalised Federated Learning: A Combinational Approach
- Algorithm Fairness in AI for Medicine and Healthcare
- Preliminary Steps Towards Federated Sentiment Classification
- Recyclable Gaussian Processes