| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and Trends® in Machine Learning 14 (1–2), 1-210, 2021 | 6498 | 2021 |
| Tackling the objective inconsistency problem in heterogeneous federated optimization J Wang, Q Liu, H Liang, G Joshi, HV Poor Advances in neural information processing systems 33, 7611-7623, 2020 | 1390 | 2020 |
| Client selection in federated learning: Convergence analysis and power-of-choice selection strategies YJ Cho, J Wang, G Joshi arXiv preprint arXiv:2010.01243, 2020 | 467 | 2020 |
| A Field Guide to Federated Optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 381 | 2021 |
| Cooperative SGD: A unified framework for the design and analysis of communication-efficient SGD algorithms J Wang, G Joshi arXiv preprint arXiv:1808.07576, 2018 | 337 | 2018 |
| Cooperative SGD: A unified framework for the design and analysis of local-update SGD algorithms J Wang, G Joshi Journal of Machine Learning Research 22 (213), 1-50, 2021 | 297 | 2021 |
| Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD J Wang, G Joshi arXiv preprint arXiv:1810.08313, 2018 | 268 | 2018 |
| On the delay-storage trade-off in content download from coded distributed storage systems G Joshi, Y Liu, E Soljanin IEEE Journal on Selected Areas in Communications 32 (5), 989-997, 2014 | 220 | 2014 |
| Mariana Raykova, Dawn Song, Weikang Song, Sebastian U P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth …, 2021 | 203 | 2021 |
| Slow and stale gradients can win the race: Error-runtime trade-offs in distributed SGD S Dutta, G Joshi, S Ghosh, P Dube, P Nagpurkar International Conference on Artificial Intelligence and Statistics, 803-812, 2018 | 201 | 2018 |
| MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling J Wang, AK Sahu, Z Yang, G Joshi, S Kar arXiv preprint arXiv:1905.09435, 2019 | 194 | 2019 |
| Towards understanding biased client selection in federated learning YJ Cho, J Wang, G Joshi International Conference on Artificial Intelligence and Statistics, 10351-10375, 2022 | 193 | 2022 |
| Using straggler replication to reduce latency in large-scale parallel computing D Wang, G Joshi, G Wornell ACM SIGMETRICS Performance Evaluation Review 43 (3), 7-11, 2015 | 171 | 2015 |
| Efficient redundancy techniques for latency reduction in cloud systems G Joshi, E Soljanin, G Wornell ACM Transactions on Modeling and Performance Evaluation of Computing Systems …, 2017 | 161 | 2017 |
| Efficient task replication for fast response times in parallel computation D Wang, G Joshi, G Wornell The 2014 ACM international conference on Measurement and modeling of …, 2014 | 143 | 2014 |
| Accelerating Deep Learning by Focusing on the Biggest Losers AH Jiang, DLK Wong, G Zhou, DG Andersen, J Dean, GR Ganger, ... arXiv preprint arXiv:1910.00762, 2019 | 126 | 2019 |
| Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning YJ Cho, A Manoel, G Joshi, R Sim, D Dimitriadis arXiv preprint arXiv:2204.12703, 2022 | 122 | 2022 |
| Adaptive quantization of model updates for communication-efficient federated learning D Jhunjhunwala, A Gadhikar, G Joshi, YC Eldar ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 116 | 2021 |
| Coding for fast content download G Joshi, Y Liu, E Soljanin arXiv preprint arXiv:1210.3012, 2012 | 114 | 2012 |
| A Novel Framework for the Analysis and Design of Heterogeneous Federated Learning J Wang, Q Liu, H Liang, G Joshi, HV Poor IEEE Transactions on Signal Processing 69, 5234-5249, 2021 | 88 | 2021 |
Jianyu WangResearch Scientist at AppleVerified email at apple.com
