Collaborative learning from an optimization and game theory view
Event details
| Date | 01.09.2023 |
| Hour | 14:00 › 16:00 |
| Speaker | Dongyang Fan |
| Location | |
| Category | Conferences - Seminars |
EDIC candidacy exam
Exam president: Prof. Anne-Marie Kermarrec
Thesis advisor: Prof. Martin Jaggi
Co-examiner: Prof. Daniel Kuhn
Abstract
In this write-up, we consider a decentralized learning setting where multiple agents collaborate to enhance their respective local objectives in terms of test performance and convergence speed. We investigate methods for incentivizing agents to participate in collaboration and share truthful information, optimal collaborative strategies for agents, and the extent to which agents can derive benefits from such collaboration. These questions are addressed through a dual prism, encompassing both optimization and game-theoretical frameworks.
Background papers
- Mathieu Even, Laurent Massoulié, Kevin Scaman. On Sample Optimality in Personalized Collaborative and Federated Learning. Neurips 2022: https://proceedings.neurips.cc/paper_files/paper/2022/hash/01cea7793f3c68af2e4989fc66bf8fb0-Abstract-Conference.html
- Kate Donahue, Jon Kleinberg. Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation. AAAI 2021.
https://ojs.aaai.org/index.php/AAAI/article/view/16669
- Florian E. Dorner and Nikola Konstantinov and Georgi Pashaliev and Martin Vechev. Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. Arxiv 2023
https://arxiv.org/abs/2305.16272
Exam president: Prof. Anne-Marie Kermarrec
Thesis advisor: Prof. Martin Jaggi
Co-examiner: Prof. Daniel Kuhn
Abstract
In this write-up, we consider a decentralized learning setting where multiple agents collaborate to enhance their respective local objectives in terms of test performance and convergence speed. We investigate methods for incentivizing agents to participate in collaboration and share truthful information, optimal collaborative strategies for agents, and the extent to which agents can derive benefits from such collaboration. These questions are addressed through a dual prism, encompassing both optimization and game-theoretical frameworks.
Background papers
- Mathieu Even, Laurent Massoulié, Kevin Scaman. On Sample Optimality in Personalized Collaborative and Federated Learning. Neurips 2022: https://proceedings.neurips.cc/paper_files/paper/2022/hash/01cea7793f3c68af2e4989fc66bf8fb0-Abstract-Conference.html
- Kate Donahue, Jon Kleinberg. Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation. AAAI 2021.
https://ojs.aaai.org/index.php/AAAI/article/view/16669
- Florian E. Dorner and Nikola Konstantinov and Georgi Pashaliev and Martin Vechev. Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. Arxiv 2023
https://arxiv.org/abs/2305.16272
Practical information
- General public
- Free