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[해외논문] FedMes: Speeding Up Federated Learning With Multiple Edge Servers

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society, v.39 no.12, 2021년, pp.3870 - 3885  

Han, Dong-Jun (Korea Advanced Institute of Science and Technology (KAIST), School of Electrical Engineering, Daejoen, South Korea) ,  Choi, Minseok (Jeju National University, Jeju, South Korea) ,  Park, Jungwuk (Korea Advanced Institute of Science and Technology (KAIST), School of Electrical Engineering, Daejoen, South Korea) ,  Moon, Jaekyun (Korea Advanced Institute of Science and Technology (KAIST), School of Electrical Engineering, Daejoen, South Korea)

Abstract AI-Helper 아이콘AI-Helper

We consider federated learning (FL) with multiple wireless edge servers having their own local coverage. We focus on speeding up training in this increasingly practical setup. Our key idea is to utilize the clients located in the overlapping coverage areas among adjacent edge servers (ESs); in the m...

참고문헌 (35)

  1. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.. Gradient-based learning applied to document recognition. Proceedings of the IEEE, vol.86, no.11, 2278-2324.

  2. arXiv 1805 09767 Local SGD converges fast and communicates little stich 2018 

  3. J Mach Learn Res Communication-efficient algorithms for statistical optimization zhang 2013 14 3321 

  4. Amiri, Mohammad Mohammadi, Gündüz, Deniz, Kulkarni, Sanjeev R., Poor, H. Vincent. Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge. IEEE transactions on wireless communications, vol.20, no.6, 3643-3658.

  5. Learning multiple layers of features from tiny images krizhevsky 2009 

  6. ArXiv 1708 07747 Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms xiao 2017 

  7. arXiv 1806 00582 Federated learning with non-IID data zhao 2018 

  8. Sattler, Felix, Wiedemann, Simon, Müller, Klaus-Robert, Samek, Wojciech. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data. IEEE transactions on neural networks and learning systems, vol.31, no.9, 3400-3413.

  9. Proc Int Conf Learn Represent On the convergence of FedAvg on non-IID data li 2020 

  10. Proc Int Conf Artif Intell Statist FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization reisizadeh 2020 2021 

  11. arXiv 1811 11479 Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data jeong 2018 

  12. Mao, Yuyi, You, Changsheng, Zhang, Jun, Huang, Kaibin, Letaief, Khaled B.. A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications surveys and tutorials, vol.19, no.4, 2322-2358.

  13. Nguyen, Ti Ti, Ha, Vu Nguyen, Le, Long Bao, Schober, Robert. Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems. IEEE transactions on wireless communications, vol.19, no.1, 293-309.

  14. ETSI White Paper Mobile edge computing’a key technology towards 5G hu 2015 11 1 

  15. Han, Dong-Jun, Sohn, Jy-Yong, Moon, Jaekyun. Hierarchical Broadcast Coding: Expediting Distributed Learning at the Wireless Edge. IEEE transactions on wireless communications, vol.20, no.4, 2266-2281.

  16. So, Jinhyun, Güler, Başak, Avestimehr, A. Salman. Byzantine-Resilient Secure Federated Learning. IEEE journal on selected areas in communications : a publication of the IEEE Communications Society, vol.39, no.7, 2168-2181.

  17. 10.1109/ICC47138.2019.9123209 

  18. arXiv 1902 01046 Towards federated learning at scale: System design bonawitz 2019 

  19. Proc Int Conf Mach Learn A unified theory of decentralized sgd with changing topology and local updates koloskova 2020 5381 

  20. arXiv 1610 02527 Federated optimization: Distributed machine learning for on-device intelligence kone?ný 2016 

  21. Wang, Shiqiang, Tuor, Tiffany, Salonidis, Theodoros, Leung, Kin K., Makaya, Christian, He, Ting, Chan, Kevin. Adaptive Federated Learning in Resource Constrained Edge Computing Systems. IEEE journal on selected areas in communications : a publication of the IEEE Communications Society, vol.37, no.6, 1205-1221.

  22. Nguyen, Hung T., Sehwag, Vikash, Hosseinalipour, Seyyedali, Brinton, Christopher G., Chiang, Mung, Vincent Poor, H.. Fast-Convergent Federated Learning. IEEE journal on selected areas in communications : a publication of the IEEE Communications Society, vol.39, no.1, 201-218.

  23. arXiv 1908 07873 Federated learning: Challenges, methods, and future directions li 2019 

  24. Amiri, Mohammad Mohammadi, Gündüz, Deniz. Federated Learning Over Wireless Fading Channels. IEEE transactions on wireless communications, vol.19, no.5, 3546-3557.

  25. Chen, Mingzhe, Yang, Zhaohui, Saad, Walid, Yin, Changchuan, Poor, H. Vincent, Cui, Shuguang. A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks. IEEE transactions on wireless communications, vol.20, no.1, 269-283.

  26. Proc NIPS Workshop Private Multi-Party Mach Learn Federated learning: Strategies for improving communication efficiency kone?ný 2016 

  27. Yang, Kai, Jiang, Tao, Shi, Yuanming, Ding, Zhi. Federated Learning via Over-the-Air Computation. IEEE transactions on wireless communications, vol.19, no.3, 2022-2035.

  28. Proc 20th Int Conf Artif Intell Stat Communication-efficient learning of deep networks from decentralized data mcmahan 2017 54 1273 

  29. 10.1109/INFOCOM.2019.8737464 

  30. 10.1109/ICASSP40776.2020.9054634 

  31. Lim, Wei Yang Bryan, Luong, Nguyen Cong, Hoang, Dinh Thai, Jiao, Yutao, Liang, Ying-Chang, Yang, Qiang, Niyato, Dusit, Miao, Chunyan. Federated Learning in Mobile Edge Networks: A Comprehensive Survey. IEEE Communications surveys and tutorials, vol.22, no.3, 2031-2063.

  32. arXiv 2007 03273 Coded computing for federated learning at the edge prakash 2020 

  33. 10.1109/ICC40277.2020.9148862 

  34. Proc 3rd Workshop Bayesian Deep NIPS Workshop Fully decentralized federated learning lalitha 2018 1 

  35. arXiv 1905 06731 BrainTorrent: A Peer-to-Peer environment for decentralized federated learning roy 2019 

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