Details of Research Outputs

TitleA Joint Learning and Communications Framework for Federated Learning Over Wireless Networks
Author (Name in English or Pinyin)
Chen, Mingzhe1,2; Yang, Zhaohui3; Saad, Walid4; Yin, Changchuan5; Poor, H. Vincent2; Cui, Shuguang1,6
Date Issued2021
Indexed BySCIE
Firstlevel Discipline信息科学与系统科学
Education discipline科技类
Published range国外学术期刊
Volume Issue Pages卷: 20 期: 1 页: 269-283
[1] M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, "Performance optimization of federated learning over wireless networks," in Proc. IEEE Global Commun. Conf., Waikoloa, HI, USA, Dec. 2019, pp. 1-6.
[2] M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, "Artificial neural networks-based machine learning for wireless networks: A tutorial," IEEE Commun. Surveys Tuts., vol. 21, no. 4, pp. 3039-3071, 4th Quart., 2019.
[3] Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, "Application of machine learning in wireless networks: Key techniques and open issues," IEEE Commun. Surveys Tuts., vol. 21, no. 4, pp. 3072-3108, 4th Quart., 2019.
[4] Y. Liu, S. Bi, Z. Shi, and L. Hanzo, "When machine learning meets big data: A wireless communication perspective," IEEE Veh. Technol. Mag., vol. 15, no. 1, pp. 63-72, Mar. 2020.
[5] K. Bonawitz et al., "Towards federated learning at scale: System design," in Proc. Syst. Mach. Learn. Conf., Stanford, CA, USA, 2019, pp. 1-15.
[6] V. Smith, C. K. Chiang, M. Sanjabi, and A. S. Talwalkar, "Federated multi-task learning," in Proc. Adv. Neural Inf. Process. Syst., Long Beach, CA, USA, Dec. 2017, pp. 4424-4434.
[7] X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, "In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning," IEEE Netw., vol. 33, no. 5, pp. 156-165, Sep. 2019.
[8] W. Saad, M. Bennis, and M. Chen, "A vision of 6G wireless systems: Applications, trends, technologies, and open research problems," IEEE Netw., vol. 34, no. 3, pp. 134-142, May 2020.
[9] E. Jeong, S. Oh, J. Park, H. Kim, M. Bennis, and S.-L. Kim, "Multihop federated private data augmentation with sample compression," in Proc. Int. Joint Conf. Artif. Intell. Workshop Federated Mach. Learn. User Privacy Data Confidentiality, Macao, China, Aug. 2019, pp. 1-8.
[10] Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, "Energy efficient federated learning over wireless communication networks," 2019, arXiv:1911.02417. [Online]. Available:
[11] T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, "Federated learning: Challenges, methods, and future directions," IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50-60, May 2020.
[12] J. Konecný, H. B. McMahan, D. Ramage, and P. Richtárik, "Federated optimization: Distributed machine learning for on-device intelligence," Oct. 2016, arXiv:1610.02527. [Online]. Available:
[13] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, "Communication-efficient learning of deep networks from decentralized data," in Proc. Conf. Mach. Learn. Res., Fort Lauderdale, FL, USA, Apr. 2017, pp. 1-10.
[14] M. Chen, O. Semiari, W. Saad, X. Liu, and C. Yin, "Federated echo state learning for minimizing breaks in presence in wireless virtual reality networks," IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 177-191, Jan. 2020.
[15] J. Konecný, B. McMahan, and D. Ramage, "Federated optimization: Distributed optimization beyond the datacenter," Nov. 2015, arXiv:1511.03575. [Online]. Available:
[16] S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, "Distributed federated learning for ultra-reliable low-latency vehicular communications," IEEE Trans. Commun., vol. 68, no. 2, pp. 1146-1159, Feb. 2020.
[17] S. Ha, J. Zhang, O. Simeone, and J. Kang, "Coded federated computing in wireless networks with straggling devices and imperfect CSI," in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Paris, France, Jul. 2019, pp. 2649-2653.
[18] O. Habachi, M.-A. Adjif, and J.-P. Cances, "Fast uplink grant for NOMA: A federated learning based approach," Mar. 2019, arXiv:1904.07975. [Online]. Available:
[19] J. Park, S. Samarakoon, M. Bennis, and M. Debbah, "Wireless network intelligence at the edge," Proc. IEEE, vol. 107, no. 11, pp. 2204-2239, Nov. 2019.
[20] Q. Zeng, Y. Du, K. Huang, and K. K. Leung, "Energy-efficient radio resource allocation for federated edge learning," in Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), Dublin, Ireland, Jun. 2020, pp. 1-6.
[21] S. Wang et al., "Adaptive federated learning in resource constrained edge computing systems," IEEE J. Sel. Areas Commun., vol. 37, no. 6, pp. 1205-1221, Jun. 2019.
[22] Z. Zhao, C. Feng, H. H. Yang, and X. Luo, "Federated-learningenabled intelligent fog radio access networks: Fundamental theory, key techniques, and future trends," IEEE Wireless Commun., vol. 27, no. 2, pp. 22-28, Apr. 2020.
[23] T. T. Vu, D. T. Ngo, N. H. Tran, H. Q. Ngo, M. N. Dao, and R. H. Middleton, "Cell-free massive MIMO for wireless federated learning," IEEE Trans. Wireless Commun., early access, Jun. 24, 2020, doi: 10.1109/TWC.2020.3002988.
[24] N. H. Tran, W. Bao, A. Zomaya, M. N. H. Nguyen, and C. S. Hong, "Federated learning over wireless networks: Optimization model design and analysis," in Proc. IEEE Conf. Comput. Commun. (INFOCOM), Paris, France, Apr. 2019, pp. 1387-1395.
[25] M. Chen, H. V. Poor, W. Saad, and S. Cui, "Convergence time optimization for federated learning over wireless networks," 2020, arXiv:2001.07845. [Online]. Available:
[26] H. H. Yang, Z. Liu, T. Q. S. Quek, and H. V. Poor, "Scheduling policies for federated learning in wireless networks," IEEE Trans. Commun., vol. 68, no. 1, pp. 317-333, Jan. 2020.
[27] S. Bi, J. Lyu, Z. Ding, and R. Zhang, "Engineering radio maps for wireless resource management," IEEE Wireless Commun., vol. 26, no. 2, pp. 133-141, Apr. 2019.
[28] C. Hennig and M. Kutlukaya, "Some thoughts about the design of loss functions," Revstat Stat. J., vol. 5, no. 1, pp. 19-39, Mar. 2007.
[29] J. Konecny, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon, "Federated learning: Strategies for improving communication efficiency," in Proc. NIPS Workshop Private Multi-Party Mach. Learn., Barcelona, Spain, Dec. 2016, pp. 1-10.
[30] Y. Xi, A. Burr, J. Wei, and D. Grace, "A general upper bound to evaluate packet error rate over quasi-static fading channels," IEEE Trans. Wireless Commun., vol. 10, no. 5, pp. 1373-1377, May 2011.
[31] Y. Pan, C. Pan, Z. Yang, and M. Chen, "Resource allocation for D2D communications underlaying a NOMA-based cellular network," IEEE Wireless Commun. Lett., vol. 7, no. 1, pp. 130-133, Feb. 2018.
[32] M. P. Friedlander and M. Schmidt, "Hybrid deterministic-stochastic methods for data fitting," SIAM J. Sci. Comput., vol. 34, no. 3, pp. A1380-A1405, Jan. 2012.
[33] M. Mahdian and Q. Yan, "Online bipartite matching with random arrivals: An approach based on strongly factor-revealing LPs," in Proc. ACM Symp. Theory Comput., San Jose, CA, USA, Jun. 2011, pp. 597-606.
[34] R. Jonker and T. Volgenant, "Improving the Hungarian assignment algorithm," Oper. Res. Lett., vol. 5, no. 4, pp. 171-175, Oct. 1986.
[35] N. Landman, K. Moore, and J. Khim. Hungarian Maximum Matching Algorithm. Accessed: Sep. 2020. [Online]. Available:
[36] Y. LeCun. The MNIST Database of Handwritten Digits. Accessed: Sep. 2020. [Online]. Available:
[37] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionSchool of Science and Engineering
Corresponding AuthorCui, Shuguang
1.Chinese Univ Hong Kong, Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China
2.Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA
3.Kings Coll London, Dept Engn, London WC2R 2LS, England
4.Virginia Tech, Bradley Dept Elect & Comp Engn, Wireless VT, Blacksburg, VA 24060 USA
5.Beijing Univ Posts & Telecommun, Beijing Key Lab Network Syst Architecture & Conve, Beijing 100876, Peoples R China
6.Chinese Univ Hong Kong, Future Network Intelligence Inst, Shenzhen 518172, Peoples R China
Recommended Citation
GB/T 7714
Chen, Mingzhe,Yang, Zhaohui,Saad, Walidet al. A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2021.
APA Chen, Mingzhe, Yang, Zhaohui, Saad, Walid, Yin, Changchuan, Poor, H. Vincent, & Cui, Shuguang. (2021). A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS.
MLA Chen, Mingzhe,et al."A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021).
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Chen, Mingzhe]'s Articles
[Yang, Zhaohui]'s Articles
[Saad, Walid]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Mingzhe]'s Articles
[Yang, Zhaohui]'s Articles
[Saad, Walid]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Mingzhe]'s Articles
[Yang, Zhaohui]'s Articles
[Saad, Walid]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.