AdaSTopk: Adaptive federated shuffle model based on differential privacy
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Xuehui Du | Nan Wang | Wenjuan Wang | Aodi Liu | Qiantao Yang | Xiangyu Wu
[1] Aytaç Altan,et al. Artificial Intelligence-Based Robust Hybrid Algorithm Design and Implementation for Real-Time Detection of Plant Diseases in Agricultural Environments , 2022, Biology.
[2] N. Holalkere,et al. Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence , 2021, Nature Machine Intelligence.
[3] Antonious M. Girgis,et al. On the Rényi Differential Privacy of the Shuffle Model , 2021, CCS.
[4] M. Yoshikawa,et al. FLAME: Differentially Private Federated Learning in the Shuffle Model , 2020, AAAI.
[5] Philip S. Yu,et al. LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy , 2020, IJCAI.
[6] Borja Balle,et al. Private Summation in the Multi-Message Shuffle Model , 2020, CCS.
[7] Richard Nock,et al. Advances and Open Problems in Federated Learning , 2019, Found. Trends Mach. Learn..
[8] Qiang Yang,et al. Federated Machine Learning , 2019, ACM Trans. Intell. Syst. Technol..
[9] Úlfar Erlingsson,et al. Prochlo: Strong Privacy for Analytics in the Crowd , 2017, SOSP.
[10] Srinivas Devadas,et al. Riffle: An Efficient Communication System With Strong Anonymity , 2016, Proc. Priv. Enhancing Technol..
[11] Za'er Salim Abo-Hammour,et al. Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm , 2014, Inf. Sci..
[12] Aaron Roth,et al. The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..
[13] Omar Abu Arqub,et al. Optimization Solution of Troesch’s and Bratu’s Problems of Ordinary Type Using Novel Continuous Genetic Algorithm , 2014 .