Federated Learning with Matched Averaging H Wang, M Yurochkin, Y Sun, D Papailiopoulos, Y Khazaeni ICLR 2020 - International Conference on Learning Representations, 2020 | 1096 | 2020 |
Fedml: A research library and benchmark for federated machine learning C He, S Li, J So, X Zeng, M Zhang, H Wang, X Wang, P Vepakomma, ... arXiv preprint arXiv:2007.13518, 2020 | 538* | 2020 |
Attack of the tails: Yes, you really can backdoor federated learning H Wang, K Sreenivasan, S Rajput, H Vishwakarma, S Agarwal, J Sohn, ... Advances in Neural Information Processing Systems 33, 16070-16084, 2020 | 537 | 2020 |
Atomo: Communication-efficient learning via atomic sparsification H Wang, S Sievert, S Liu, Z Charles, D Papailiopoulos, S Wright Advances in neural information processing systems 31, 2018 | 369 | 2018 |
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 | 337 | 2021 |
Draco: Byzantine-resilient distributed training via redundant gradients L Chen, H Wang, Z Charles, D Papailiopoulos International Conference on Machine Learning, 903-912, 2018 | 284* | 2018 |
DETOX: A redundancy-based framework for faster and more robust gradient aggregation S Rajput, H Wang, Z Charles, D Papailiopoulos Advances in Neural Information Processing Systems 32, 2019 | 122 | 2019 |
Trustllm: Trustworthiness in large language models L Sun, Y Huang, H Wang, S Wu, Q Zhang, C Gao, Y Huang, W Lyu, ... arXiv preprint arXiv:2401.05561, 2024 | 79 | 2024 |
Erasurehead: Distributed gradient descent without delays using approximate gradient coding H Wang, Z Charles, D Papailiopoulos arXiv preprint arXiv:1901.09671, 2019 | 57 | 2019 |
Pufferfish: Communication-efficient models at no extra cost H Wang, S Agarwal, D Papailiopoulos Proceedings of Machine Learning and Systems 3, 365-386, 2021 | 42 | 2021 |
On the utility of gradient compression in distributed training systems S Agarwal, H Wang, S Venkataraman, D Papailiopoulos Proceedings of Machine Learning and Systems 4, 652-672, 2022 | 40 | 2022 |
MPCFormer: fast, performant and private Transformer inference with MPC D Li, R Shao, H Wang, H Guo, EP Xing, H Zhang arXiv preprint arXiv:2211.01452, 2022 | 39 | 2022 |
Adaptive gradient communication via critical learning regime identification S Agarwal, H Wang, K Lee, S Venkataraman, D Papailiopoulos Proceedings of Machine Learning and Systems 3, 55-80, 2021 | 37* | 2021 |
Rare Gems: Finding Lottery Tickets at Initialization K Sreenivasan, J Sohn, L Yang, M Grinde, A Nagle, H Wang, K Lee, ... NeurIPS 2022, 2022 | 31 | 2022 |
The effect of network width on the performance of large-batch training L Chen, H Wang, J Zhao, D Papailiopoulos, P Koutris Advances in neural information processing systems 31, 2018 | 24 | 2018 |
Llm360: Towards fully transparent open-source llms Z Liu, A Qiao, W Neiswanger, H Wang, B Tan, T Tao, J Li, Y Wang, S Sun, ... arXiv preprint arXiv:2312.06550, 2023 | 19 | 2023 |
Efficient federated learning on knowledge graphs via privacy-preserving relation embedding aggregation K Zhang, Y Wang, H Wang, L Huang, C Yang, X Chen, L Sun arXiv preprint arXiv:2203.09553, 2022 | 17 | 2022 |
Slimpajama-dc: Understanding data combinations for llm training Z Shen, T Tao, L Ma, W Neiswanger, J Hestness, N Vassilieva, ... arXiv preprint arXiv:2309.10818, 2023 | 14 | 2023 |
Federated learning as variational inference: A scalable expectation propagation approach H Guo, P Greengard, H Wang, A Gelman, Y Kim, EP Xing arXiv preprint arXiv:2302.04228, 2023 | 9 | 2023 |
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness D Li, H Wang, E Xing, H Zhang NeurIPS 2022, 2022 | 7 | 2022 |