October 7, 2022
@
12:00 PM
–
1:00 PM
Room 1163 Mechanical Engineering
Raed Al Kontar
A critical change is happening in today’s Internet of Things (IoT). The computational power at
the edge device is steadily increasing. AI chips are rapidly infiltrating the market. Mobile phones’
processing power is becoming comparable to laptops available for everyday use. Tesla’s autopilot system
has 150 million times more computing power than Apollo 11, and small local computers such as Raspberry Pis have become commonplace in manufacturing systems. This change opens a new paradigm of data
analytics within IoT, one that exploits edge compute resources to process more of users’ data where it’s
created. More specifically, with the availability of some computing resources at each client, clients can
execute small computations locally, instead of sharing all raw data to a central cloud, and then only share
the minimum information needed to collaboratively extract knowledge and build smart analytics while
keeping their personal data stored locally. This paradigm shift sets forth many intrinsic advantages,
including privacy, cost-effectiveness, diversity, fairness, and reduced computation and latency, among
many others. In this talk, I term this future of IoT as the “Internet of Federated Things (IoFT)” and discuss
our recent efforts in federated data analytics aimed at bringing this future into reality. Specifically, I will
present federated analytics approaches to tackle three central challenges within IoFT: (1) Personalization:
where participants borrow strength from each other yet retain their own individualized models. (2)
Fairness: to allow solutions with good representation power across groups of heterogeneous participants
(3) Distributed uncertainty quantification (UQ): where we bring federated analytics to correlated
paradigms beyond empirical risk minimization to quantify uncertainty. I end the talk by describing our
prototyping efforts to generate real-life IoFT data
Bio: Raed Al Kontar is an assistant professor in the Industrial & Operations engineering department at the
University of Michigan and an affiliate with the Michigan Institute for Data Science. Raed’s research
focuses on distributed and federated probabilistic modeling. Raed obtained an undergraduate degree in
civil & environmental engineering and mathematics from the American University of Beirut in 2014
followed by a master’s degree in statistics in 2017 and a Ph.D. degree in Industrial & System Engineering
in 2018, both from the University of Wisconsin-Madison. Raed has seven best paper awards from the
INFORMS Quality, Statistics & Reliability section, INFORMS Data Mining section, and IISE. Raed received
the NSF CAREER award in 2022. His research is currently supported by both NSF and NIH.