Tiny Machine Learning for Concept Drift

by   Simone Disabato, et al.

Tiny Machine Learning (TML) is a new research area whose goal is to design machine and deep learning techniques able to operate in Embedded Systems and IoT units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices. Interestingly, the related literature mainly focused on reducing the computational and memory demand of the inference phase of machine and deep learning models. At the same time, the training is typically assumed to be carried out in Cloud or edge computing systems (due to the larger memory and computational requirements). This assumption results in TML solutions that might become obsolete when the process generating the data is affected by concept drift (e.g., due to periodicity or seasonality effect, faults or malfunctioning affecting sensors or actuators, or changes in the users' behavior), a common situation in real-world application scenarios. For the first time in the literature, this paper introduces a Tiny Machine Learning for Concept Drift (TML-CD) solution based on deep learning feature extractors and a k-nearest neighbors classifier integrating a hybrid adaptation module able to deal with concept drift affecting the data-generating process. This adaptation module continuously updates (in a passive way) the knowledge base of TML-CD and, at the same time, employs a Change Detection Test to inspect for changes (in an active way) to quickly adapt to concept drift by removing the obsolete knowledge. Experimental results on both image and audio benchmarks show the effectiveness of the proposed solution, whilst the porting of TML-CD on three off-the-shelf micro-controller units shows the feasibility of what is proposed in real-world pervasive systems.


page 1

page 2

page 3

page 4


A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification

In real-world applications, the process generating the data might suffer...

Adaptation Strategies for Automated Machine Learning on Evolving Data

Automated Machine Learning (AutoML) systems have been shown to efficient...

A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices

A practical issue of edge AI systems is that data distributions of train...

A Model Drift Detection and Adaptation Framework for 5G Core Networks

The advent of Fifth Generation (5G) and beyond 5G networks (5G+) has rev...

LEAF: Navigating Concept Drift in Cellular Networks

Operational networks commonly rely on machine learning models for many t...

Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors

We present a straightforward statistical test to detect certain violatio...

Reactive Soft Prototype Computing for Concept Drift Streams

The amount of real-time communication between agents in an information s...

Please sign up or login with your details

Forgot password? Click here to reset