Thema4839: Unterschied zwischen den Versionen
Wt7447 (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „{{Abschlussarbeit |Titel=Federated Aggregation of Random Forests |Abschlussarbeitstyp=Master |Betreuer=Florian Leiser |Forschungsgruppe=Critical Information In…“) |
Wt7447 (Diskussion | Beiträge) |
||
(7 dazwischenliegende Versionen desselben Benutzers werden nicht angezeigt) | |||
Zeile 1: | Zeile 1: | ||
{{Abschlussarbeit | {{Abschlussarbeit | ||
− | |Titel=Federated Aggregation of Random Forests | + | |Titel=Artificial Intelligence: Federated Aggregation of Random Forests |
− | |Abschlussarbeitstyp=Master | + | |Abschlussarbeitstyp=Bachelor, Master |
|Betreuer=Florian Leiser | |Betreuer=Florian Leiser | ||
|Forschungsgruppe=Critical Information Infrastructures | |Forschungsgruppe=Critical Information Infrastructures | ||
− | |Abschlussarbeitsstatus= | + | |Abschlussarbeitsstatus=In Bearbeitung |
− | |Beginn= | + | |Beginn=2022/03/01 |
− | |Beschreibung DE=Background: Federated Learning (FL) is an emerging paradigm which enables different clients to train a common model while maintaining data privacy. Within a FL network, every client trains a local Machine Learning (ML) model at his site. A central server aggregates the weights of different local models resulting in a common model while the raw data remains at each site. This aggregation works well for parametric approaches like Linear Regression and Neural Networks where an average or other aggregation of the values can be computed easily. However, it is still unclear how non-parametric ML approaches, like Decision Trees and Random Forests can be aggregated in such a way. | + | |Beschreibung DE=<p><strong>Background:</strong></p> |
+ | <p>Federated Learning (FL) is an emerging paradigm which enables different clients to train a common model while maintaining data privacy. Within a FL network, every client trains a local Machine Learning (ML) model at his site. A central server aggregates the weights of different local models resulting in a common model while the raw data remains at each site. This aggregation works well for parametric approaches like Linear Regression and Neural Networks where an average or other aggregation of the values can be computed easily. However, it is still unclear how non-parametric ML approaches, like Decision Trees and Random Forests can be aggregated in such a way.</p> | ||
− | Objective(s): The aim of this thesis is to develop an aggregation of Random Forests across different clients. Within the thesis you might develop and pursue multiple approaches on how an aggregation of Random Trees might be done. A comparison of the different approaches needs to be conducted. First approaches on how to tackle this approach are presented below but you are invited to try and follow your own ideas. | + | <p><strong>Objective(s):</strong></p> |
+ | <p>The aim of this thesis is to develop an aggregation of Random Forests across different clients. Within the thesis you might develop and pursue multiple approaches on how an aggregation of Random Trees might be done. A comparison of the different approaches needs to be conducted. First approaches on how to tackle this approach are presented below but you are invited to try and follow your own ideas.</p> | ||
− | Literature: | + | <p><br /><strong>Literature:</strong></p> |
− | Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977. (https://arxiv.org/abs/1912.04977) | + | <ul> |
+ | <li>Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977. (https://arxiv.org/abs/1912.04977)</li> | ||
+ | <li>Liu, Y., Liu, Y., Liu, Z., Liang, Y., Meng, C., Zhang, J., & Zheng, Y. (2020). Federated forest. IEEE Transactions on Big Data. (https://arxiv.org/abs/1905.10053)</li> | ||
+ | <li>Wu, Y., Cai, S., Xiao, X., Chen, G., & Ooi, B. C. (2020). Privacy preserving vertical federated learning for tree-based models. arXiv preprint arXiv:2008.06170. (https://arxiv.org/pdf/2008.06170.pdf)</li> | ||
+ | </ul> | ||
+ | |Beschreibung EN=<p><strong>Background:</strong></p> | ||
+ | <p>Federated Learning (FL) is an emerging paradigm which enables different clients to train a common model while maintaining data privacy. Within a FL network, every client trains a local Machine Learning (ML) model at his site. A central server aggregates the weights of different local models resulting in a common model while the raw data remains at each site. This aggregation works well for parametric approaches like Linear Regression and Neural Networks where an average or other aggregation of the values can be computed easily. However, it is still unclear how non-parametric ML approaches, like Decision Trees and Random Forests can be aggregated in such a way.</p> | ||
− | + | <p><strong>Objective(s):</strong></p> | |
+ | <p>The aim of this thesis is to develop an aggregation of Random Forests across different clients. Within the thesis you might develop and pursue multiple approaches on how an aggregation of Random Trees might be done. A comparison of the different approaches needs to be conducted. First approaches on how to tackle this approach are presented below but you are invited to try and follow your own ideas.</p> | ||
− | Wu, Y., Cai, S., Xiao, X., Chen, G., & Ooi, B. C. (2020). Privacy preserving vertical federated learning for tree-based models. arXiv preprint arXiv:2008.06170. (https://arxiv.org/pdf/2008.06170.pdf) | + | <p><br /><strong>Literature:</strong></p> |
+ | <ul> | ||
+ | <li>Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977. (https://arxiv.org/abs/1912.04977)</li> | ||
+ | <li>Liu, Y., Liu, Y., Liu, Z., Liang, Y., Meng, C., Zhang, J., & Zheng, Y. (2020). Federated forest. IEEE Transactions on Big Data. (https://arxiv.org/abs/1905.10053)</li> | ||
+ | <li>Wu, Y., Cai, S., Xiao, X., Chen, G., & Ooi, B. C. (2020). Privacy preserving vertical federated learning for tree-based models. arXiv preprint arXiv:2008.06170. (https://arxiv.org/pdf/2008.06170.pdf)</li> | ||
+ | </ul> | ||
}} | }} |
Aktuelle Version vom 29. März 2022, 07:50 Uhr
Abschlussarbeitstyp: Bachelor, Master
Betreuer: Florian Leiser
Forschungsgruppe: Critical Information Infrastructures
Archivierungsnummer: 4839
Abschlussarbeitsstatus: In Bearbeitung
Beginn:
01. März 2022
Abgabe: unbekannt
Background:
Federated Learning (FL) is an emerging paradigm which enables different clients to train a common model while maintaining data privacy. Within a FL network, every client trains a local Machine Learning (ML) model at his site. A central server aggregates the weights of different local models resulting in a common model while the raw data remains at each site. This aggregation works well for parametric approaches like Linear Regression and Neural Networks where an average or other aggregation of the values can be computed easily. However, it is still unclear how non-parametric ML approaches, like Decision Trees and Random Forests can be aggregated in such a way.
Objective(s):
The aim of this thesis is to develop an aggregation of Random Forests across different clients. Within the thesis you might develop and pursue multiple approaches on how an aggregation of Random Trees might be done. A comparison of the different approaches needs to be conducted. First approaches on how to tackle this approach are presented below but you are invited to try and follow your own ideas.
Literature:
- Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977. (https://arxiv.org/abs/1912.04977)
- Liu, Y., Liu, Y., Liu, Z., Liang, Y., Meng, C., Zhang, J., & Zheng, Y. (2020). Federated forest. IEEE Transactions on Big Data. (https://arxiv.org/abs/1905.10053)
- Wu, Y., Cai, S., Xiao, X., Chen, G., & Ooi, B. C. (2020). Privacy preserving vertical federated learning for tree-based models. arXiv preprint arXiv:2008.06170. (https://arxiv.org/pdf/2008.06170.pdf)