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

Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Steven Wu, Jinfeng Yi

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)26048-26067
Number of pages20
JournalProceedings of Machine Learning Research
StatePublished - 2022
Event39th International Conference on Machine Learning, ICML 2022 - Baltimore, United States
Duration: Jul 17 2022Jul 23 2022

Bibliographical note

Funding Information:
We thank the anonymous reviewers for valuable feedback on the merit of the work, and helpful suggestions on improving the presentation. Z. S. Wu was supported in part by the NSF CNS #2120603, a CMU CyLab 2021 grant, a Google Faculty Research Award, and a Mozilla Research Grant. M. Hong, X. Chen and X. Zhang are supported in part by NSF grants CIF-1910385, CMMI-1727757 and AFOSR grant 19RT0424.

Publisher Copyright:
Copyright © 2022 by the author(s)


Dive into the research topics of 'Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy'. Together they form a unique fingerprint.

Cite this