Suggestions
Mi Zhang
Associate Professor, Michigan State University
Mi Zhang is currently an Associate Professor of Computer Science and Engineering at The Ohio State University, where he directs the AIoT and Machine Learning Systems Lab.14 He previously held positions at Michigan State University, first as an Assistant Professor of ECE & CSE from 2014 to 2020, and then as an Associate Professor from 2020 to 2022.3
Zhang's research focuses on Edge AI, Artificial Intelligence of Things (AIoT), machine learning systems, and mobile health.1 He received his B.S. in Electrical Engineering from Peking University and his Ph.D. in Computer Engineering from the University of Southern California.3
Some of Zhang's notable achievements include:
- Winning the MSU Innovation of the Year Award in 2020 for his smart hearing aids invention.12
- Receiving the ACM SenSys Best Paper Award in 2021.1
- Being awarded the Inaugural USC ECE SIPI Distinguished Alumni Award in 2023.14
- Winning several research awards, including the Facebook Faculty Research Award (2020) and Amazon AWS Machine Learning Research Award (2019).13
Zhang has made significant contributions to the field of AI and IoT, developing algorithms for on-device deep learning, noise removal in smart hearing aids, and model compression for enhancing the efficiency of deep learning models.1
Highlights
Large Language Models (LLMs) have demonstrated remarkable capabilities in many important tasks and have the potential to make a substantial impact on our society. Such capabilities, however, come with considerable resource demands, highlighting the strong need to develop effective techniques for addressing the efficiency challenges posed by LLMs.
We are very excited to share our survey on Efficient LLMs. In this survey, we provide a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from model-centric, data-centric, and framework-centric perspective, respectively. We have also created a GitHub repository where we compile the papers featured in this survey, and will actively maintain this repository and incorporate new research as it emerges. We hope our survey can serve as a valuable resource to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.
This survey is a collaborative effort between OSU, Imperial College London, Michigan State University, Amazon AWS AI, Google Research, Boson AI, Microsoft Research Asia, and University of Michigan. Huge shoutout to all the coauthors: Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, and Mosharaf Chowdhury.
- Github Repository: https://lnkd.in/gyXU_D2m
- Paper: https://lnkd.in/gcbSjXf4
#LLMs, #EfficientAI, #MLSys
There is a significant relevance of federated learning (FL) in the realm of Artificial Intelligence of Things (AIoT). However, most of existing FL works are not conducted on datasets collected from authentic IoT devices that capture unique modalities and inherent challenges of IoT data.
We are very excited to introduce FedAIoT, an FL benchmark for AIoT to fill this critical gap.
- FedAIoT includes eight well-chosen datatsets collected from a wide range of IoT devices from smartwatch, smartphone and Wi-Fi routers, to drones, smart home sensors, and head-mounted device. These datasets cover unique IoT modalities and target representative applications of AIoT.
- FedAIoT also includes a unified end-to-end FL framework that simplifies benchmarking the performance of FL algorithms on the included datasets.
- Our benchmark results shed light on the opportunities and challenges of FL for AIoT.
- We hope FedAIoT could serve as an invaluable resource to foster advancements in the important field of FL for AIoT.
FedAIoT is a collaborative effort between #OSU, #USC, #MSU, #Yonsei, and #Meta. Huge shoutout to all the coauthors: Samiul Alam, Tuo Zhang, Tiantian Feng, Hui Shen, Zhichao CAO, Dong Zhao, JeongGil Ko, Kiran Somasundaram, Shrikanth Narayanan, Salman Avestimehr.
- Paper: https://lnkd.in/gW5HSUr2
- Github Repo: https://lnkd.in/g78MjEts
#AIoT, #federatedlearning, #IoT
