| Deepcache: Principled cache for mobile deep vision M Xu, M Zhu, Y Liu, FX Lin, X Liu Proceedings of the 24th annual international conference on mobile computing …, 2018 | 257 | 2018 |
| A first look at deep learning apps on smartphones M Xu, J Liu, Y Liu, FX Lin, Y Liu, X Liu The World Wide Web Conference, 2125-2136, 2019 | 219 | 2019 |
| Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data C Yang, Q Wang, M Xu, Z Chen, K Bian, Y Liu, X Liu Proceedings of the Web Conference 2021, 935-946, 2021 | 211* | 2021 |
| DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning M Xu, F Qian, M Zhu, F Huang, S Pushp, X Liu IEEE Transactions on Mobile Computing, 2019 | 154* | 2019 |
| Personal llm agents: Insights and survey about the capability, efficiency and security Y Li, H Wen, W Wang, X Li, Y Yuan, G Liu, J Liu, W Xu, X Wang, Y Sun, ... arXiv preprint arXiv:2401.05459, 2024 | 116 | 2024 |
| From cloud to edge: a first look at public edge platforms M Xu, Z Fu, X Ma, L Zhang, Y Li, F Qian, S Wang, K Li, J Yang, X Liu Proceedings of the 21st ACM internet measurement conference, 37-53, 2021 | 111 | 2021 |
| Deeptype: On-device deep learning for input personalization service with minimal privacy concern M Xu, F Qian, Q Mei, K Huang, X Liu Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2018 | 92 | 2018 |
| A survey of resource-efficient llm and multimodal foundation models M Xu, W Yin, D Cai, R Yi, D Xu, Q Wang, B Wu, Y Zhao, C Yang, S Wang, ... arXiv preprint arXiv:2401.08092, 2024 | 88 | 2024 |
| ShuffleDog: characterizing and adapting user-perceived latency of android apps G Huang, M Xu, FX Lin, Y Liu, Y Ma, S Pushp, X Liu IEEE Transactions on Mobile Computing 16 (10), 2913-2926, 2017 | 78 | 2017 |
| Efficient federated learning for modern nlp D Cai, Y Wu, S Wang, FX Lin, M Xu Proceedings of the 29th Annual International Conference on Mobile Computing …, 2023 | 76* | 2023 |
| A comprehensive benchmark of deep learning libraries on mobile devices Q Zhang, X Li, X Che, X Ma, A Zhou, M Xu, S Wang, Y Ma, X Liu Proceedings of the ACM Web Conference 2022, 3298-3307, 2022 | 62* | 2022 |
| Melon: Breaking the memory wall for resource-efficient on-device machine learning Q Wang, M Xu, C Jin, X Dong, J Yuan, X Jin, G Huang, Y Liu, X Liu Proceedings of the 20th Annual International Conference on Mobile Systems …, 2022 | 61 | 2022 |
| Edgemoe: Fast on-device inference of moe-based large language models R Yi, L Guo, S Wei, A Zhou, S Wang, M Xu arXiv preprint arXiv:2308.14352, 2023 | 60 | 2023 |
| Approximate query service on autonomous iot cameras M Xu, X Zhang, Y Liu, G Huang, X Liu, FX Lin Proceedings of the 18th International Conference on Mobile Systems …, 2020 | 60 | 2020 |
| Mandheling: Mixed-precision on-device dnn training with dsp offloading D Xu, M Xu, Q Wang, S Wang, Y Ma, K Huang, G Huang, X Jin, X Liu Proceedings of the 28th Annual International Conference on Mobile Computing …, 2022 | 53 | 2022 |
| Tiansuan constellation: An open research platform S Wang, Q Li, M Xu, X Ma, A Zhou, Q Sun 2021 IEEE International Conference on Edge Computing (EDGE), 94-101, 2021 | 52 | 2021 |
| Llmcad: Fast and scalable on-device large language model inference D Xu, W Yin, X Jin, Y Zhang, S Wei, M Xu, X Liu arXiv preprint arXiv:2309.04255, 2023 | 48 | 2023 |
| Hierarchical federated learning through LAN-WAN orchestration J Yuan, M Xu, X Ma, A Zhou, X Liu, S Wang arXiv preprint arXiv:2010.11612, 2020 | 48 | 2020 |
| FwdLLM: Efficient Federated Finetuning of Large Language Models with Perturbed Inferences M Xu, D Cai, Y Wu, X Li, S Wang 2024 USENIX Annual Technical Conference (USENIX ATC 24), 579-596, 2024 | 46* | 2024 |
| Video Analytics with Zero-streaming Cameras M Xu, T Xu, Y Liu, FX Lin 2021 {USENIX} Annual Technical Conference ({USENIX}{ATC} 21), 459-472, 2021 | 46* | 2021 |
