Wearable Internet of Things (wIoT) together with deep learning is revolutionizing the smart health and wellbeing applications. Predominantly IoT devices are good at acquiring medical data and later sending it cloud. Edge-based deep learning infuse intelligence in terms of processing, analysis and inference on edge near wearables. Edge deep learning not only offload the cloud but also ensure high-throughput, low-latency solutions. With edge deep learning, the data is processed on edge leading to improved privacy and security as now the data is not transferred to cloud for inference. Resource constraints on the edge and endpoint IoT devices pose challenges in adopting deep learning solutions. Systems and algorithms deployed in health and fitness devices require research on efficient approaches for signal sensing, analysis and prediction. Recently, deep learning models are increasingly deployed on wearable and edge devices for neural prediction and inference. Modern smartwatches and smart textiles are health as well as fitness device. Deep learning on edge also allows for personalization of medical solutions that enhances the user’s experience. Increasingly more wearables in health and fitness now rely on voice-based assistants. Recently, several custom chips with medical machine learning functionalities are developed to further advance edge deep learning. We live in exciting times when wearables and deep learning are growing in parallel and together creating tremendous impact on smart health & fitness devices, systems and services.



This workshop invites researchers from academia and industry to submit their current research for fostering academia-industry collaboration. The scope of this workshop includes but not limited to the following topics:

• Resource-constrained deep learning for sensing, analysis, and interpretation in IoT healthcare

• Low latency inference on edge for smart health

• Privacy-preserving machine learning, federated learning, on-device training, and differential privacy for smart health

• Knowledge transfer and model compressions for smart health

• Hardware optimization, sparsity, quantization, power savings and algorithmic trade-offs for on-device training and inference

• Recent advances in Edge, Fog and Mist computing for machine learning in healthcare

• Context-aware pervasive system and E2E deep learning for health and fitness applications

• Scalability, privacy, and security aspects of edge-based machine learning

• Emerging applications of edge intelligence in for personalized health and fitness monitoring, tracking and control

• Self-supervised, semi-supervised and unsupervised ML for smart health

• Edge-coordinated health data analysis, visualization and interoperability

• Role of big data in edge-based machine learning for smart health & fitness applications

• Edge devices with custom hardware for Neuromorphic AI and cognitive computing in smart health

Workshop Format
EdgeDL 2021 program will start with Keynote talk followed by paper presentations. We conclude workshop with a Panel discussion.

Keynote talk

Panel discussion

Technical Program Committee
· Shaad Mahmud, University of Massachusetts Dartmouth, USA

· Nikil Dutt, University of California, Irvine, USA

· Ankesh Jain, IIT Delhi, India

· Axel Jantsch, TU Wien, Austria

· Abhinav Misra, Educational Testing Service (ETS), USA

· Shivesh Ranjan, Apple Inc., USA

· Fatemeh Saki, Qualcomm Inc., San Diego, USA

· Puneet Goyal, IIT Ropar, India

· Pasi Liljeberg, University of Turku, Finland

· C. P. Ravikumar, Texas Instrument, India

· Geng Yang, Zhejiang University, China



Important Dates :
Paper submission deadline: 15 May 2021

Notification of paper acceptance: 2 June 2021

Submission of camera-ready: 10 June 2021 (firm deadline)

Registration Deadline: TBD

Camera Ready Submission:
Final Camera-Ready Submission Link: TBD

Technical Chairs:
Harishchandra Dubey, Microsoft Corporation, USA (harishchandra.dubey@microsoft.com)

Xiaoqian Jiang, Associate Professor and Director of Center for Secure Artificial intelligence For Healthcare (SAFE), UTHealth School of Biomedical Informatics (SBMI), Houston, USA (Xiaoqian.Jiang@uth.tmc.edu)

Arindam Pal, CSIRO's Data61 and Cyber Security CRC, Sydney, NSW, Australia (arindamp@gmail.com)

Amir M. Rahmani, University of California Irvine, USA (a.rahmani@uci.edu)

Kunal Mankodiya, University of Rhode Island, USA (kunalm@uri.edu)

Publicity Chair:
Cosimo Ieracitano, University Mediterranea of Reggio Calabria, Italy

Rabindra Kumar Barik, KIIT University, India [rabindrafca@kiit.ac.in]

Technical Program Committee:
Shaad Mahmud, University of Massachusetts Dartmouth, USA

Nikil Dutt, University of California, Irvine, USA

Arti Ramesh, SUNY Binghamton, Binghamton, USA

Ankesh Jain, IIT Delhi, India

Axel Jantsch, TU Wien, Austria

Abhinav Misra, Educational Testing Service (ETS), USA

Shivesh Ranjan, Apple Inc., USA

Fatemeh Saki, Qualcomm Inc., San Diego, USA

Puneet Goyal, IIT Ropar, India

Pasi Liljeberg, University of Turku, Finland

C. P. Ravikumar, Texas Instrument, India

Geng Yang, Zhejiang University, China

Vinay Kumar, MNNIT Allahabad, India

Contacts:
For questions related to IEEE EdgeDL 2021 workshop, please contact the workshop organizers.

Previous EdgeDL Editions:
EdgeDL workshops were co-located with IEEE/ACM CHASE conference in 2018 and 2019 in vibrant Washington D.C., USA and IEEE SmartComp 2020.

https://sites.google.com/view/ieee-edgedl-2020/

https://sites.google.com/view/edgedl-ieee-acm-chase-2019/home

https://sites.google.com/view/chase-dl-edge-iot/home