Federated Learning for Edge AI: Realistic Hardware-Software System Co-Design
Federated Learning (FL) is essential in the rapidly expanding Internet-of-Things (IoT) landscape. The addition of nearly 2 billion devices in 2023 has intensified the need for Edge AI to adopt efficient FL solutions, thus enabling decentralized ML model training on edge devices with a focus on data privacy.
My research focuses on Hierarchical Federated Learning (HFL), a variant of FL that leverages edge servers that are physically closer to end-user devices. HFL is better fit for real-world scenarios since it reduces communication costs and is more scalable.
This talk will explore the synergies between hardware-software co-design and real-world considerations such as user mobility and semi-supervised learning solutions in and FL, showing how these innovations can lead to more efficient, scalable, and privacy-centric IoT ecosystems.
Biography: Allen-Jasmin Farcas is a fourth-year Ph.D. student at The University of Texas at Austin. His research interests include federated learning systems, edge AI systems, and hardware/software co-design for edge AI. Farcas received his bachelor’s degree in computer engineering in 2019 from Politehnica University Timisoara.
Date and Time
Location
Hosts
Registration
- Date: 18 Dec 2023
- Time: 12:00 PM to 01:00 PM
- All times are (UTC+02:00) Bucharest
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- Bulevardul Vasile Pârvan 2, Timișoara
- Timișoara, Timis
- Romania
- Building: Biblioteca Centrală a Universității Politehnica Timișoara
- Room Number: K1
- Click here for Map
Speakers
Allen-Jasmin Farcaș of The University of Texas at Austin, USA
Allen-Jasmin Farcas is a fourth-year Ph.D. student at The University of Texas at Austin. His research interests include federated learning systems, edge AI systems, and hardware/software co-design for edge AI. Farcas received his bachelor’s degree in computer engineering in 2019 from Politehnica University Timisoara.