Comments on "Federated Learning with Differential Privacy: Algorithms and Performance Analysis"
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916281417990144 |
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| author | Talaei, Mahtab Izadi, Iman |
| author_facet | Talaei, Mahtab Izadi, Iman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05858 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comments on "Federated Learning with Differential Privacy: Algorithms and Performance Analysis" Talaei, Mahtab Izadi, Iman Distributed, Parallel, and Cluster Computing Cryptography and Security Performance 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. |
| title | Comments on "Federated Learning with Differential Privacy: Algorithms and Performance Analysis" |
| topic | Distributed, Parallel, and Cluster Computing Cryptography and Security Performance |
| url | https://arxiv.org/abs/2406.05858 |