Publications

Y. Shi, K. Yang, T. Jiang, J. Zhang, and K. B. Letaief, ‘‘Communication-efficient edge AI: algorithms and systems,’’ IEEE Commun. Surveys Tuts., vol. 22, no. 4, pp. 2167-2191, Fourthquarter 2020. [paper]

K. Yang, Y. Shi, Y. Zhou, Z. Yang, L. Fu, and W. Chen, “Federated machine learning for intelligent IoT via reconfigurable intelligent surface,” IEEE Netw., vol. 34, no. 5, pp. 16-22, September/October 2020.

K. Yang, Y. Zhou, Z. Yang, and Y. Shi, “Communication-efficient edge AI inference over wireless networks,” ZTE Commun., vol. 18, no. 2, pp. 31-39, 2020.

K. Yang, Y. Shi, W. Yu, and Z. Ding, ‘‘Energy-efficient processing and robust wireless cooperative transmission for edge inference,’’ IEEE Internet Things J., vol. 7, no. 10, pp. 9456-9470, Oct. 2020. [paper] [code] [ref]

K. Yang, T. Jiang, Y. Shi, and Z. Ding, ‘‘Federated learning via over-the-air computation,’’ IEEE Trans. Wireless Commun., vol. 19, no. 3, pp. 2022-2035, Mar. 2020. [paper] [code] [ref]

K. Yang, Y. Shi, and Z. Ding, ‘‘Data shuffling in wireless distributed computing via low-rank optimization," IEEE Trans. Signal Process., vol. 67, no. 12, pp. 3087-3099, Jun. 2019. [paper] [code] [ref]

K. Yang, Y. Shi, and Z. Ding, ‘‘Generalized low-rank optimization for topological cooperation in ultra-dense networks," IEEE Trans. Wireless Commun., vol. 18, no. 5, pp. 2539-2552, May 2019. [paper] [code] [ref]

J. Dong, K. Yang, and Y. Shi, ‘‘Ranking from crowdsourced pairwise comparisons via smoothed Riemannian optimization," in ACM Trans. Knowl. Discov. Data, vol. 14, no. 2, pp. 1-26, Feb. 2020.

J. Dong, K. Yang, and Y. Shi, ‘‘Blind demixing for low-latency communication," in IEEE Trans. Wireless Commun., vol. 18, no. 2, pp. 897-911, Feb. 2019.

X. Chen, S. Zhou, K. Yang, H. Fan, Z. Feng, Z. Chen, H. Wang, and Y. Wang, “Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning”, in International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021, Virtual, Jul. 2021.

K. Yang, T. Fan, T. Chen, Y. Shi and Q. Yang, “A quasi-Newton method based vertical federated learning framework for logistic regression,” in Neural Inf. Process. Syst. (NeurIPS) Workshops on Federated Learning for Data Privacy and Confidentiality, Vancouver, Canada, Dec. 2019. [paper] [poster] [code]

K. Yang, T. Jiang, Y. Shi, and Z. Ding, ‘‘Federated learning based on over-the-air computation,’’ in Proc. IEEE Int. Conf. Commun. (ICC), Shanghai, China, May 2019.

T. Jiang, K. Yang, and Y. Shi, ‘‘Pliable data shuffling for on-device distributed learning,’’ in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Brighton, UK, May 2019.

K. Yang, Y. Shi, and Z. Ding, ‘‘Low-rank optimization for data shuffling in wireless distributed computing,’’ in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), Alberta, Canada, Apr. 2018.

J. Dong, K. Yang, and Y. Shi, ‘‘Blind demixing for low-latency communication,’’ in Proc. IEEE Wireless Commun. Networking Conf. (WCNC), Barcelona, Spain, Apr. 2018.

K. Yang, Y. Shi, and Z. Ding, ‘‘Generalized matrix completion for low complexity transceiver processing in cache-aided Fog-RAN via the Burer-Monteiro approach,’’ in Proc. IEEE Global Conf. Signal and Inf. Process. (GlobalSIP), Montreal, Canada, Nov. 2017.

J. Dong, K. Yang, and Y. Shi, ‘‘Ranking from crowdsourced pairwise comparisons via smoothed matrix manifold optimization,’’ in ICDM Workshops on Data-driven Discovery of Models (D3M), New Orleans, Louisiana, USA, Nov. 2017.

K. Yang, Y. Shi, J. Zhang, Z. Ding and K. B. Letaief, ‘‘A low-rank approach for interference management in dense wireless networks,’’ in Proc. IEEE Global Conf. Signal and Inf. Process.(GlobalSIP), Washington, DC, Dec. 2016.

K. Yang, Y. Shi, and Z. Ding, ‘‘Low-rank matrix completion for mobile edge caching in Fog-RAN via Riemannian optimization,’’ in Proc. IEEE Global Commun. Conf. (Globecom), Washington, DC, Dec. 2016.