Arizona State University Researcher Receives Grant to Enhance Security and Intelligence of Edge Devices

In today’s interconnected world, edge devices are becoming increasingly integral to our daily lives. These devices, such as smartphones, doorbell cameras, and fitness trackers, are now storing significant amounts of sensitive data locally, making security a top priority. Hokeun Kim, an assistant professor at Arizona State University, is taking on this challenge with a grant from ATTO Research.

Kim’s research focuses on developing a specialized toolset for edge devices, tailored to enable federated learning. Federated learning allows devices to learn from user data while maintaining privacy and security. With the rise of AI-capable edge devices, the need for robust security measures has never been more critical.

“Edge devices were once considered secure, as they sent data to centralized servers for processing,” explains Kim. “But as these devices become more powerful and store sensitive data locally, they are at risk of security breaches.”

Kim’s grant aims to create a middleware solution that empowers developers to build smart and secure edge devices. By implementing secure federated learning, Kim hopes to equip developers with the tools needed to harness AI’s power while protecting data privacy.

“Our goal is to integrate existing security standards and cryptography protocols to enable efficient federated learning on edge devices without compromising security,” says Kim.

The potential applications of secure federated learning are vast. In healthcare, edge devices could analyze patient data locally and share insights with medical professionals without compromising patient privacy. Kim’s work also extends to smart city infrastructure and law enforcement, where edge devices play a crucial role.

Ross Maciejewski, director of the School of Computing and Augmented Intelligence at ASU, emphasizes the significance of Kim’s research in enabling edge devices to leverage AI while safeguarding sensitive data.