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

06/25/2021
by   Xinwei Zhang, et al.
0

Providing privacy protection has been one of the primary motivations of Federated Learning (FL). Recently, there has been a line of work on incorporating the formal privacy notion of differential privacy with FL. To guarantee the client-level differential privacy in FL algorithms, the clients' transmitted model updates have to be clipped before adding privacy noise. Such clipping operation is substantially different from its counterpart of gradient clipping in the centralized differentially private SGD and has not been well-understood. In this paper, we first empirically demonstrate that the clipped FedAvg can perform surprisingly well even with substantial data heterogeneity when training neural networks, which is partly because the clients' updates become similar for several popular deep architectures. Based on this key observation, we provide the convergence analysis of a differential private (DP) FedAvg algorithm and highlight the relationship between clipping bias and the distribution of the clients' updates. To the best of our knowledge, this is the first work that rigorously investigates theoretical and empirical issues regarding the clipping operation in FL algorithms.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/28/2021

FeO2: Federated Learning with Opt-Out Differential Privacy

Federated learning (FL) is an emerging privacy-preserving paradigm, wher...
02/15/2022

Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy

Federated learning (FL) that enables distributed clients to collaborativ...
06/17/2021

Locally Differentially Private Federated Learning: Efficient Algorithms with Tight Risk Bounds

Federated learning (FL) is a distributed learning paradigm in which many...
06/13/2021

Understanding the Interplay between Privacy and Robustness in Federated Learning

Federated Learning (FL) is emerging as a promising paradigm of privacy-p...
04/26/2022

Federated Stochastic Primal-dual Learning with Differential Privacy

Federated learning (FL) is a new paradigm that enables many clients to j...
06/13/2021

DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning

In this paper, we focus on facilitating differentially private quantized...
03/08/2022

LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data

Federated learning (FL) and split learning (SL) are the two popular dist...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.