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DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset

https://doi.org/10.1109/TMC.2023.3337016

Liu, Weijie; Zhang, Xiaoxi; Duan, Jingpu; Joe-Wong, Carlee; Zhou, Zhi; Chen, Xu (January 2024, IEEE Transactions on Mobile Computing)

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AdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning

https://doi.org/10.1109/IWQoS57198.2023.10188807

Liu, Weijie; Zhang, Xiaoxi; Duan, Jingpu; Joe-Wong, Carlee; Zhou, Zhi; Chen, Xu (June 2023, 2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS))

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Federated Learning at the Edge: An Interplay of Mini-batch Size and Aggregation Frequency

https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10226109

Liu, Weijie; Zhang, Xiaoxi; Duan, Jingpu; Joe-Wong, Carlee; Zhou, Zhi; Chen, Xu (May 2023, IEEE)

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The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition

https://doi.org/10.1093/jamia/ocac165

Kuo, Tsung-Ting; Jiang, Xiaoqian; Tang, Haixu; Wang, XiaoFeng; Harmanci, Arif; Kim, Miran; Post, Kai; Bu, Diyue; Bath, Tyler; Kim, Jihoon; et al (September 2022, Journal of the American Medical Informatics Association)

Abstract Concerns regarding inappropriate leakage of sensitive personal information as well as unauthorized data use are increasing with the growth of genomic data repositories. Therefore, privacy and security of genomic data have become increasingly important and need to be studied. With many proposed protection techniques, their applicability in support of biomedical research should be well understood. For this purpose, we have organized a community effort in the past 8 years through the integrating data for analysis, anonymization and sharing consortium to address this practical challenge. In this article, we summarize our experience from these competitions, report lessons learned from the events in 2020/2021 as examples, and discuss potential future research directions in this emerging field.
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Isolation disrupts social interactions and destabilizes brain development in bumblebees

https://doi.org/10.1016/j.cub.2022.04.066

Wang, Z. Yan; McKenzie-Smith, Grace C.; Liu, Weijie; Cho, Hyo Jin; Pereira, Talmo; Dhanerawala, Zahra; Shaevitz, Joshua W.; Kocher, Sarah D. (June 2022, Current Biology)

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HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework

https://doi.org/10.1109/CLOUD53861.2021.00098

Widanage, Chathura; Liu, Weijie; Li, Jiayu; Chen, Hongbo; Wang, XiaoFeng; Tang, Haixu; Fox, Judy (September 2021, 2021 IEEE 14th International Conference on Cloud Computing (CLOUD))

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Practical and Efficient in-Enclave Verification of Privacy Compliance

https://doi.org/10.1109/DSN48987.2021.00052

Liu, Weijie; Wang, Wenhao; Chen, Hongbo; Wang, XiaoFeng; Lu, Yaosong; Chen, Kai; Wang, Xinyu; Shen, Qintao; Chen, Yi; Tang, Haixu (June 2021, Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN))

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Convergent and complementary selection shaped gains and losses of eusociality in sweat bees

https://doi.org/10.1038/s41559-023-02001-3

Jones, Beryl M.; Rubin, Benjamin E.; Dudchenko, Olga; Kingwell, Callum J.; Traniello, Ian M.; Wang, Z. Yan; Kapheim, Karen M.; Wyman, Eli S.; Adastra, Per A.; Liu, Weijie; et al (April 2023, Nature Ecology & Evolution)

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