Federated Learning with Differential Privacy
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929234080956416 |
|---|---|
| author | Banse, Adrien Kreischer, Jan Jürgens, Xavier Oliva i |
| author_facet | Banse, Adrien Kreischer, Jan Jürgens, Xavier Oliva i |
| contents | Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving client's private data from being shared among different parties. Nevertheless, private information can still be divulged by analyzing uploaded parameter weights from clients. In this report, we showcase our empirical benchmark of the effect of the number of clients and the addition of differential privacy (DP) mechanisms on the performance of the model on different types of data. Our results show that non-i.i.d and small datasets have the highest decrease in performance in a distributed and differentially private setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02230 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Learning with Differential Privacy Banse, Adrien Kreischer, Jan Jürgens, Xavier Oliva i Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing I.2.11 Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving client's private data from being shared among different parties. Nevertheless, private information can still be divulged by analyzing uploaded parameter weights from clients. In this report, we showcase our empirical benchmark of the effect of the number of clients and the addition of differential privacy (DP) mechanisms on the performance of the model on different types of data. Our results show that non-i.i.d and small datasets have the highest decrease in performance in a distributed and differentially private setting. |
| title | Federated Learning with Differential Privacy |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing I.2.11 |
| url | https://arxiv.org/abs/2402.02230 |