| Client selection for federated learning with heterogeneous resources in mobile edge T Nishio, R Yonetani ICC 2019-2019 IEEE international conference on communications (ICC), 1-7, 2019 | 1806 | 2019 |
| Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data S Itahara, T Nishio, Y Koda, M Morikura, K Yamamoto IEEE Transactions on Mobile Computing, 2021 | 317 | 2021 |
| Hybrid-FL for wireless networks: Cooperative learning mechanism using non-IID data N Yoshida, T Nishio, M Morikura, K Yamamoto, R Yonetani ICC 2020-2020 IEEE International Conference On Communications (ICC), 1-7, 2020 | 277* | 2020 |
| Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud T Nishio, R Shinkuma, T Takahashi, NB Mandayam Proceedings of the first international workshop on Mobile cloud computing …, 2013 | 227 | 2013 |
| Extreme ultra-reliable and low-latency communication J Park, S Samarakoon, H Shiri, MK Abdel-Aziz, T Nishio, A Elgabli, ... Nature Electronics 5 (3), 133-141, 2022 | 222* | 2022 |
| Proactive received power prediction using machine learning and depth images for mmWave networks T Nishio, H Okamoto, K Nakashima, Y Koda, K Yamamoto, M Morikura, ... IEEE Journal on Selected Areas in Communications 37 (11), 2413-2427, 2019 | 117 | 2019 |
| Deep reinforcement learning-based channel allocation for wireless lans with graph convolutional networks K Nakashima, S Kamiya, K Ohtsu, K Yamamoto, T Nishio, M Morikura IEEE Access 8, 31823-31834, 2020 | 88 | 2020 |
| Handover management for mmWave networks with proactive performance prediction using camera images and deep reinforcement learning Y Koda, K Nakashima, K Yamamoto, T Nishio, M Morikura IEEE Transactions on Cognitive Communications and Networking 6 (2), 802-816, 2019 | 84 | 2019 |
| MAB-based Client Selection for Federated Learning with Uncertain Resources in Mobile Networks N Yoshida, T Nishio, M Morikura, K Yamamoto IEEE GLOBECOM Wksp on OpenMLC, 2020 | 69 | 2020 |
| Communication-efficient multimodal split learning for mmWave received power prediction Y Koda, J Park, M Bennis, K Yamamoto, T Nishio, M Morikura, ... IEEE Communications Letters 24 (6), 1284-1288, 2020 | 65 | 2020 |
| Adaptive resource discovery in mobile cloud computing W Liu, T Nishio, R Shinkuma, T Takahashi Computer Communications 50, 119-129, 2014 | 65 | 2014 |
| When wireless communications meet computer vision in beyond 5G T Nishio, Y Koda, J Park, M Bennis, K Doppler IEEE Communications Standards Magazine 5 (2), 76-83, 2021 | 60 | 2021 |
| Differentially private aircomp federated learning with power adaptation harnessing receiver noise Y Koda, K Yamamoto, T Nishio, M Morikura GLOBECOM 2020-2020 IEEE Global Communications Conference, 1-6, 2020 | 59 | 2020 |
| Reinforcement learning based predictive handover for pedestrian-aware mmWave networks Y Koda, K Yamamoto, T Nishio, M Morikura IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops …, 2018 | 59 | 2018 |
| Estimation of individual device contributions for incentivizing federated learning T Nishio, R Shinkuma, NB Mandayam 2020 IEEE Globecom Workshops (GC Wkshps, 1-6, 2020 | 39 | 2020 |
| Proactive handover based on human blockage prediction using RGB-D cameras for mmWave communications Y Oguma, T Nishio, K Yamamoto, M Morikura IEICE Transactions on Communications 99 (8), 1734-1744, 2016 | 39 | 2016 |
| Machine-learning-based throughput estimation using images for mmWave communications H Okamoto, T Nishio, M Morikura, K Yamamoto, D Murayama, K Nakahira 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 1-6, 2017 | 35 | 2017 |
| Proactive base station selection based on human blockage prediction using RGB-D cameras for mmWave communications Y Oguma, R Arai, T Nishio, K Yamamoto, M Morikura 2015 IEEE Global Communications Conference (GLOBECOM), 1-6, 2015 | 35 | 2015 |
| Decentralized and model-free federated learning: Consensus-based distillation in function space A Taya, T Nishio, M Morikura, K Yamamoto IEEE Transactions on Signal and Information Processing over Networks 8, 799-814, 2022 | 26 | 2022 |
| Packet-loss-tolerant split inference for delay-sensitive deep learning in lossy wireless networks S Itahara, T Nishio, K Yamamoto 2021 IEEE Global Communications Conference (GLOBECOM), 1-6, 2021 | 23 | 2021 |
Koji YamamotoKyoto Institute of TechnologyEmail verificata su kit.ac.jp
