add papers on NIPS

README.md

@@ -373,6 +373,8 @@ Federated Learning papers accepted by top ML(machine learning) conference and jo
 |Title                                                           |    Affiliation                                                     |    Venue             |    Year    |    Materials|
 | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------- | ---- | ------------------------------------------------------------ |
 | SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning | University at Buffalo | NeurIPS | 2023 | [[PUB](https://openreview.net/forum?id=ZdxGmJGKOo)] [[PDF](https://arxiv.org/abs/2305.19442)] [[SUPP](https://openreview.net/attachment?id=ZdxGmJGKOo&name=supplementary_material)] |
+| Mechanism Design for Collaborative Normal Mean Estimation | UW-Madison | NeurIPS | 2023 | [PUB](https://openreview.net/forum?id=yKCLfOOIL7) [PDF](https://arxiv.org/abs/2306.06351) |
+| Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity | EPFL | NeurIPS | 2023 | [PUB](https://openreview.net/forum?id=n3fPDW87is) [PDF](https://arxiv.org/abs/2309.13591) [code](https://github.com/GeovaniRizk/Robust-Distributed-Learning-Tight-Error-Bounds-and-Breakdown-Point-under-Data-Heterogeneity) |
 | Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization | UIUC | NeurIPS | 2023 | [[PUB](https://openreview.net/forum?id=9OqezkNxnX)] [[SUPP](https://openreview.net/attachment?id=9OqezkNxnX&name=supplementary_material)] |
 | Convergence Analysis of Sequential Federated Learning on Heterogeneous Data | BUPT | NeurIPS | 2023 | [[PUB](https://openreview.net/forum?id=Dxhv8Oja2V)] [[PDF](https://arxiv.org/abs/2311.03154)] [[CODE](https://github.com/liyipeng00/convergence)] |
 | Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition | MBZUAI | NeurIPS | 2023 | [[PUB](https://openreview.net/forum?id=LGKxz9clGG)] [[PDF](https://arxiv.org/abs/2310.15165)] [[CODE](https://github.com/sarapieri/fed_het)] |