Online Learning Techniques for Occupancy Detection on Resource Constrained Devices
2025 - Intel4EC 2025 : 3rd International Workshop on Intelligent and Adaptive Edge-Cloud Operations and Services. (In conjunction with IEEE International Parallel & Distributed Processing Symposium 2025)
In light of the increasing impact of climate change, the demand for energy-efficient solutions across various sectors is becoming crucial. This paper explores the feasibility of utilizing Online Learning techniques to detect room occupancy in smart buildings, addressing the challenges of privacy…
On-device Online Learning and Semantic Management of TinyML Systems
2024 - ACM Transactions on Embedded Computing Systems,Vol. 23, Issue 4
Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning (ML). While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges.This study aims to bridge the gap between…
TinyMetaFed: Efficient Federated Meta-Learning for TinyML
2023 ECML PKDD workshop track: Simplification, Compression, Efficiency, and Frugality for Artificial Intelligence (SCEFA) , Torino, Italy
The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on lowfootprint devices, such as microcontrollers. The prevalence of these miniature devices raises the question of whether aggregating their knowledge can benefit TinyML applications.…
TinyReptile: TinyML with Federated Meta-Learning
2023 International Joint Conference on Neural Networks (IJCNN) , Gold Coast, Australia
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can benefit from aggregating their knowledge.…