Understanding the Interplay between Privacy and Robustness in Federated Learning

by   Yaowei Han, et al.

Federated Learning (FL) is emerging as a promising paradigm of privacy-preserving machine learning, which trains an algorithm across multiple clients without exchanging their data samples. Recent works highlighted several privacy and robustness weaknesses in FL and addressed these concerns using local differential privacy (LDP) and some well-studied methods used in conventional ML, separately. However, it is still not clear how LDP affects adversarial robustness in FL. To fill this gap, this work attempts to develop a comprehensive understanding of the effects of LDP on adversarial robustness in FL. Clarifying the interplay is significant since this is the first step towards a principled design of private and robust FL systems. We certify that local differential privacy has both positive and negative effects on adversarial robustness using theoretical analysis and empirical verification.


page 1

page 2

page 3

page 4


Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy

Federated Learning (FL) allows multiple participants to collaboratively ...

Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy

Providing privacy protection has been one of the primary motivations of ...

Federated Learning with Noisy User Feedback

Machine Learning (ML) systems are getting increasingly popular, and driv...

DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning

Federated learning (FL) has become an emerging machine learning techniqu...

Understanding Unintended Memorization in Federated Learning

Recent works have shown that generative sequence models (e.g., language ...

Measuring Lower Bounds of Local Differential Privacy via Adversary Instantiations in Federated Learning

Local differential privacy (LDP) gives a strong privacy guarantee to be ...

FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning

As an innovative solution for privacy-preserving machine learning (ML), ...