Privacy papers in ICLR 2021

1 minute read

Published: May 07, 2021

I have curated and am beginning to read ICLR ‘21 papers related to privacy and federated learning. The list will be constantly updated with the paper summaries. Stay tuned!
Note that I wrote a simple script to scrape the links to the paper and the links may not be accurate.

Title	Summary
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Adaptive Federated Optimization
Information Laundering for Model Privacy
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning
Federated Learning Based on Dynamic Regularization
CaPC Learning: Confidential and Private Collaborative Learning
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Private Image Reconstruction from System Side Channels Using Generative Models
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning
Differentially Private Learning Needs Better Features (or Much More Data)
FedMix: Approximation of Mixup under Mean Augmented Federated Learning
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
R-GAP: Recursive Gradient Attack on Privacy
Private Post-GAN Boosting	Re-weight using MWEM the sequence of learned generators and discriminators to increase performance after training.
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
Personalized Federated Learning with First Order Model Optimization	no global model. personalize via interaction with other clients.

Seng Pei Liew

Privacy papers in ICLR 2021

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