# Comments on "Federated Learning with Differential Privacy: Algorithms and Performance Analysis"

arXiv: https://arxiv.org/abs/2406.05858 · HTML: https://mlsanity.com/paper/2406.05858/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation](https://mlsanity.com/paper/2406.18491/) (2024)

## Abstract (arXiv)

In the paper by Wei et al. ("Federated Learning with Differential Privacy: Algorithms and Performance Analysis"), the convergence performance of the proposed differential privacy algorithm in federated learning (FL), known as Noising before Model Aggregation FL (NbAFL), was studied. However, the presented convergence upper bound of NbAFL (Theorem 2) is incorrect. This comment aims to present the correct form of the convergence upper bound for NbAFL.