SEC2021 Call for Participation

Join us in San Jose, CA, on December 14 – 17, 2021 at the Symposium on Edge Computing (SEC’21) to learn about the latest research and development on edge computing. The preliminary program is now available, featuring an outstanding combination of 5 keynotes, 2 panels, 25 peer-reviewed papers, 1 poster/demo session, 1 PhD forum, and[…]

Fighting COVID-19 with federated learning

The spread of the Corona Virus Disease 2019 (COVID-19) has reached pandemic levels across the globe. There is an urgent need to develop CT apps that not only monitor but also intervene to limit COVID-19 spread while respecting user security and privacy. Sponsored by National Science Foundation, this project addresses this challenge via Federated Analytics[…]

SEC ’20: Call for papers (Deadline: June 26, 2020)

The Fifth ACM/IEEE Symposium on Edge Computing (SEC) seeks to present exciting, innovative research related to the design, implementation, analysis, evaluation, and deployment of computer systems and applications at the network edge. SEC is a forum for top researchers, engineers, students, entrepreneurs, and government officials come together under one roof to discuss the opportunities and[…]

HotEdge ’20: Call for papers (Deadline: Feb 20, 2020)

Call for Papers for the 3rd USENIX Workshop on Hot Topics in Edge Computing: due Thursday, February 20, 2020 HotEdge ‘20 (#HotEdge20)(https://lnkd.in/gBH_DKf) will take place on April 30, 2020, in Santa Clara, CA, co-located with the 2020 USENIX Conference on Operational Machine Learning. HotEdge ‘20 is the flagship workshop on edge computing (#edgecomputing) and a hot venue for researchers and practitioners from both academia and industry to discuss[…]

Congrats on Ragini’s master degree

Ragini Sharma successfully defended her master thesis on “A Study on Knowledge Transfer Techniques to Support Deep Learning on Edge Devices”. Her work studies the use of knowledge transfer techniques to support distributed deep learning that exploits the knowledge from deep networks trained in the cloud to improve the accuracy and speed of small networks[…]