On Joint Noise Scaling in Differentially Private Federated Learning with Multiple Local Steps

Mikko A. Heikkilä

Keywords: federated learning, cross-silo, differential privacy, secure aggregation, privacy-preserving federated learning

TL;DR: We show how to jointly scale the DP noise with sample-level privacy and multiple local steps using SecAgg

Abstract: Federated learning is a distributed learning setting where the main aim is to train machine learning models without having to share raw data but only what is required for learning. To guarantee training data privacy and high-utility models, differential privacy and secure aggregation techniques are often combined with federated learning. However, with fine-grained protection granularities the currently existing techniques require the parties to communicate for each local optimisation step, if they want to fully benefit from the secure aggregation in terms of the resulting formal privacy guarantees. In this paper, we show how a simple new analysis allows the parties to perform multiple local optimisation steps while still benefiting from joint noise scaling when using secure aggregation. We show that our analysis enables higher utility models with guaranteed privacy protection under limited number of communication rounds.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 3025