On-device Online Learning and Semantic Management of TinyML Systems

• URL: http://arxiv.org/abs/2405.07601v2
• Date: Wed, 15 May 2024 20:09:26 GMT
• Title: On-device Online Learning and Semantic Management of TinyML Systems
• Authors: Haoyu Ren, Xue Li, Darko Anicic, Thomas A. Runkler,
• Abstract summary: This study aims to bridge the gap between prototyping single TinyML models and developing reliable TinyML systems in production. We propose online learning to enable training on constrained devices, adapting local models towards the latest field conditions. We present semantic management for the joint management of models and devices at scale.
• Score: 8.183732025472766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This study aims to bridge the gap between prototyping single TinyML models and developing reliable TinyML systems in production: (1) Embedded devices operate in dynamically changing conditions. Existing TinyML solutions primarily focus on inference, with models trained offline on powerful machines and deployed as static objects. However, static models may underperform in the real world due to evolving input data distributions. We propose online learning to enable training on constrained devices, adapting local models towards the latest field conditions. (2) Nevertheless, current on-device learning methods struggle with heterogeneous deployment conditions and the scarcity of labeled data when applied across numerous devices. We introduce federated meta-learning incorporating online learning to enhance model generalization, facilitating rapid learning. This approach ensures optimal performance among distributed devices by knowledge sharing. (3) Moreover, TinyML's pivotal advantage is widespread adoption. Embedded devices and TinyML models prioritize extreme efficiency, leading to diverse characteristics ranging from memory and sensors to model architectures. Given their diversity and non-standardized representations, managing these resources becomes challenging as TinyML systems scale up. We present semantic management for the joint management of models and devices at scale. We demonstrate our methods through a basic regression example and then assess them in three real-world TinyML applications: handwritten character image classification, keyword audio classification, and smart building presence detection, confirming our approaches' effectiveness.

Related papers

• TinySV: Speaker Verification in TinyML with On-device Learning [2.356162747014486]
  This paper introduces a new type of adaptive TinyML solution that can be used in tasks, such as the presented textitTiny Speaker Verification (TinySV) The proposed TinySV solution relies on a two-layer hierarchical TinyML solution comprising Keyword Spotting and Adaptive Speaker Verification module. We evaluate the effectiveness and efficiency of the proposed TinySV solution on a dataset collected expressly for the task and tested the proposed solution on a real-world IoT device.
  arXiv  Detail & Related papers  (2024-06-03T17:27:40Z)
• Optimization of Lightweight Malware Detection Models For AIoT Devices [2.4947404267499587]
  Malware intrusion is a problem for Internet of Things (IoT) and Artificial Intelligence of Things (AIoT) devices. This research aims to optimize the proposed super learner meta-learning ensemble model to make it viable for low-end AIoT devices.
  arXiv  Detail & Related papers  (2024-04-06T09:30:38Z)
• TinyMetaFed: Efficient Federated Meta-Learning for TinyML [8.940139322528829]
  We introduce TinyMetaFed, a model-agnostic meta-learning framework suitable for TinyML. TinyMetaFed facilitates collaborative training of a neural network. It offers communication savings and privacy protection through partial local reconstruction and Top-P% selective communication.
  arXiv  Detail & Related papers  (2023-07-13T15:39:26Z)
• FedYolo: Augmenting Federated Learning with Pretrained Transformers [61.56476056444933]
  In this work, we investigate pretrained transformers (PTF) to achieve on-device learning goals. We show that larger scale shrinks the accuracy gaps between alternative approaches and improves robustness. Finally, it enables clients to solve multiple unrelated tasks simultaneously using a single PTF.
  arXiv  Detail & Related papers  (2023-07-10T21:08:52Z)
• TinyReptile: TinyML with Federated Meta-Learning [9.618821589196624]
  We propose TinyReptile, a simple but efficient algorithm inspired by meta-learning and online learning. We demonstrate TinyReptile on Raspberry Pi 4 and Cortex-M4 MCU with only 256-KB RAM.
  arXiv  Detail & Related papers  (2023-04-11T13:11:10Z)
• eP-ALM: Efficient Perceptual Augmentation of Language Models [70.47962271121389]
  We propose to direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning.
  arXiv  Detail & Related papers  (2023-03-20T19:20:34Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors [68.8204255655161]
  This paper compares four state-of-the-art algorithms in two real applications: gesture recognition based on accelerometer data and image classification. Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs.
  arXiv  Detail & Related papers  (2022-09-01T17:05:20Z)
• How to Manage Tiny Machine Learning at Scale: An Industrial Perspective [5.384059021764428]
  Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers. TinyML models have been developed with different structures and are often distributed without a clear understanding of their working principles. We propose a framework using Semantic Web technologies to enable the joint management of TinyML models and IoT devices at scale.
  arXiv  Detail & Related papers  (2022-02-18T10:36:11Z)
• Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks [50.68446003616802]
  Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices. We develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions. Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning.
  arXiv  Detail & Related papers  (2022-02-07T05:11:01Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.