Artificial Intelligence, Data and Robotics: Foundations, Transformations and Future Directions | 2026
Authors: Violos J.; Diamanti K.-C.; Kompatsiaris I.; Papadopoulos S.
DOI: 10.1007/978-3-032-10561-5_8
Journal: Artificial Intelligence, Data and Robotics: Foundations, Transformations and Future Directions
Year: 2026
Publisher: Springer Nature
Document Type: Book chapter
Open Access: All Open Access; Gold Open Access
Cited by: 1
Frugal machine learning (FML) refers to the practice of designing machine learning (ML) models that are efficient, cost-effective, and mindful of resource constraints. This field aims to achieve acceptable performance while minimizing the use of computational resources, time, energy, and data for both training and inference. FML strategies can be broadly categorized into input frugality, learning process frugality, and model frugality, each focusing on reducing resource consumption at different stages of the ML pipeline. This chapter explores recent advancements, applications, and open challenges in FML, emphasizing its importance for smart environments that incorporate edge computing and IoT devices, which often face strict limitations in bandwidth, energy, or latency. Technological enablers such as model compression, energy-efficient hardware, and data-efficient learning techniques are discussed, along with adaptive methods including parameter regularization, knowledge distillation, and dynamic architecture design that enable incremental model updates without full retraining. Furthermore, it provides a comprehensive taxonomy of frugal methods, discusses case studies across diverse domains, and identifies future research directions to drive innovation in this evolving field. © The Editor(s) (if applicable) and The Author(s) 2026.
Data sampling; Feature selection; Frugal machine learning; Hardware accelerators; Knowledge distillation; Pruning; Quantization