add fl in top-tier journal papers

README.md

@@ -141,6 +141,10 @@ Papers of federated learning in Nature(and its sub-journals), Cell, Science(and
 
 |Title                                                           |    Affiliation    |    Venue                    |    Year    |    Materials|
 | ------------------------------------------------------------ | ----------- | --------------------- | ---- | ------------------------------------------------------------ |
+|Selective knowledge sharing for privacy-preserving federated distillation without a good teacher | HKUST | Nat. Commun. | 2024 | [PUB](https://www.nature.com/articles/s41467-023-44383-9) [PDF](https://arxiv.org/abs/2304.01731) [CODE](https://github.com/shaojiawei07/Selective-FD) |
+|A federated learning system for precision oncology in Europe: DigiONE | IQVIA Cancer Research BV | Nat. Med. (Comment) | 2024 | [PUB](https://www.nature.com/articles/s41591-023-02715-8) |
+|Multi-client distributed blind quantum computation with the Qline architecture | Sapienza Università di Roma | Nat. Commun. | 2023 | [PUB](https://www.nature.com/articles/s41467-023-43617-0) [PDF](https://arxiv.org/abs/2306.05195) |
+|Device-independent quantum randomness–enhanced zero-knowledge proof | USTC | PNAS | 2023 | [PUB](https://www.pnas.org/doi/10.1073/pnas.2205463120) [PDF](https://arxiv.org/abs/2111.06717) [新闻](https://www.nsfc.gov.cn/publish/portal0/tab448/info90817.htm) |
 | Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning | Tsinghua University | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-43883-y)] |
 | Advocating for neurodata privacy and neurotechnology regulation | Columbia University | Nat. Protoc. (Perspective) | 2023 | [[PUB](https://www.nature.com/articles/s41596-023-00873-0)] |
 | Federated benchmarking of medical artificial intelligence with MedPerf | IHU Strasbourg; University of Strasbourg; Dana-Farber Cancer Institute; Weill Cornell Medicine; Harvard T.H. Chan School of Public Health; MIT; Intel | Nat. Mach. Intell. | 2023 | [[PUB](https://www.nature.com/articles/s42256-023-00652-2)] [[PDF](https://arxiv.org/abs/2110.01406)] [[CODE](https://github.com/mlcommons/MedPerf)] |
@@ -156,6 +160,7 @@ Papers of federated learning in Nature(and its sub-journals), Cell, Science(and
 | A federated graph neural network framework for privacy-preserving personalization | THU | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-30714-9)] [[CODE](https://github.com/wuch15/FedPerGNN)] [[解读](https://zhuanlan.zhihu.com/p/487383715)] |
 | Communication-efficient federated learning via knowledge distillation | THU | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-29763-x)] [[PDF](https://arxiv.org/abs/2108.13323)] [[CODE](https://zenodo.org/record/6383473)] |
 | Lead federated neuromorphic learning for wireless edge artificial intelligence | XMU; NTU | Nat. Commun. | 2022 | [[PUB](https://www.nature.com/articles/s41467-022-32020-w)] [[CODE](https://github.com/GOGODD/FL-EDGE-COMPUTING/releases/tag/federated_learning)] [[解读](https://zhuanlan.zhihu.com/p/549087420)] |
+| A novel decentralized federated learning approach to train on globally  distributed, poor quality, and protected private medical data | University of Wollongong | Sci. Rep. | 2022 | [PUB](https://www.nature.com/articles/s41598-022-12833-x) |
 | Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence | HUST | Nat. Mach. Intell. | 2021 | [[PUB](https://www.nature.com/articles/s42256-021-00421-z)] [[PDF](https://arxiv.org/abs/2111.09461)] [[CODE](https://github.com/HUST-EIC-AI-LAB/UCADI)] |
 | Federated learning for predicting clinical outcomes in patients with COVID-19 | MGH radiology and Harvard Medical School | Nat. Med. | 2021 | [[PUB](https://www.nature.com/articles/s41591-021-01506-3)] [[CODE](https://www.nature.com/articles/s41591-021-01506-3#code-availability)] |
 | Adversarial interference and its mitigations in privacy-preserving collaborative machine learning | Imperial College London; TUM;  OpenMined | Nat. Mach. Intell.(Perspective) | 2021 | [[PUB](https://www.nature.com/articles/s42256-021-00390-3)] |