Zhang, Xin, Fang, Minghong, Liu, Zhuqing, Yang, Haibo, Liu, Jia, and Zhu, Zhengyuan. NET-FLEET: achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data. Retrieved from https://par.nsf.gov/biblio/10379065. MobiHoc '22: Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing . Web. doi:10.1145/3492866.3549723.

Zhang, Xin, Fang, Minghong, Liu, Zhuqing, Yang, Haibo, Liu, Jia, & Zhu, Zhengyuan. NET-FLEET: achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data. MobiHoc '22: Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, (). Retrieved from https://par.nsf.gov/biblio/10379065. https://doi.org/10.1145/3492866.3549723

Zhang, Xin, Fang, Minghong, Liu, Zhuqing, Yang, Haibo, Liu, Jia, and Zhu, Zhengyuan. "NET-FLEET: achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data". MobiHoc '22: Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (). Country unknown/Code not available. https://doi.org/10.1145/3492866.3549723. https://par.nsf.gov/biblio/10379065.

@article{osti_10379065,
place = {Country unknown/Code not available}, title = {NET-FLEET: achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data}, url = {https://par.nsf.gov/biblio/10379065}, DOI = {10.1145/3492866.3549723}, abstractNote = {}, journal = {MobiHoc '22: Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing}, author = {Zhang, Xin and Fang, Minghong and Liu, Zhuqing and Yang, Haibo and Liu, Jia and Zhu, Zhengyuan}, }