Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)

• URL: http://arxiv.org/abs/2409.12112v1
• Date: Wed, 18 Sep 2024 16:31:19 GMT
• Title: Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)
• Authors: Tashfain Ahmed, Josh Siegel,
• Abstract summary: We demonstrate that strategic data reduction can maintain high performance while significantly reducing bandwidth, energy, computation, and storage costs. The framework identifies Minimum Viable Data (MVD) to optimize efficiency across resource-constrained environments without sacrificing performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces the Pareto Data Framework, an approach for identifying and selecting the Minimum Viable Data (MVD) required for enabling machine learning applications on constrained platforms such as embedded systems, mobile devices, and Internet of Things (IoT) devices. We demonstrate that strategic data reduction can maintain high performance while significantly reducing bandwidth, energy, computation, and storage costs. The framework identifies Minimum Viable Data (MVD) to optimize efficiency across resource-constrained environments without sacrificing performance. It addresses common inefficient practices in an IoT application such as overprovisioning of sensors and overprecision, and oversampling of signals, proposing scalable solutions for optimal sensor selection, signal extraction and transmission, and data representation. An experimental methodology demonstrates effective acoustic data characterization after downsampling, quantization, and truncation to simulate reduced-fidelity sensors and network and storage constraints; results shows that performance can be maintained up to 95\% with sample rates reduced by 75\% and bit depths and clip length reduced by 50\% which translates into substantial cost and resource reduction. These findings have implications on the design and development of constrained systems. The paper also discusses broader implications of the framework, including the potential to democratize advanced AI technologies across IoT applications and sectors such as agriculture, transportation, and manufacturing to improve access and multiply the benefits of data-driven insights.

Related papers

• Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning [39.73152182572741]
  This paper proposes a novel framework, named Quantized Split Federated Fine-Tuning Large AI Model (SFLAM) By partitioning the training load between edge devices and servers, SFLAM can facilitate the operation of large models on devices. SFLAM incorporates quantization management, power control, and bandwidth allocation strategies to enhance training efficiency.
  arXiv  Detail & Related papers  (2025-04-12T07:55:11Z)
• Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams [10.61303879393919]
  Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making. IoT systems are often deployed in sensitive environments such as households and offices, where they may inadvertently expose identifiable information. This paper provides a technical interpretation of data minimization in the context of sensor streams, explores practical methods for implementation, and addresses the challenges involved.
  arXiv  Detail & Related papers  (2025-03-07T18:35:11Z)
• AI-in-the-Loop Sensing and Communication Joint Design for Edge Intelligence [65.29835430845893]
  We propose a framework that enhances edge intelligence through AI-in-the-loop joint sensing and communication. A key contribution of our work is establishing an explicit relationship between validation loss and the system's tunable parameters. We show that our framework reduces communication energy consumption by up to 77 percent and sensing costs measured by the number of samples by up to 52 percent.
  arXiv  Detail & Related papers  (2025-02-14T14:56:58Z)
• Data-driven Modality Fusion: An AI-enabled Framework for Large-Scale Sensor Network Management [0.49998148477760973]
  This paper introduces a novel sensing paradigm called Data-driven Modality Fusion (DMF) By leveraging correlations between timeseries data from different sensing modalities, the proposed DMF approach reduces the number of physical sensors required for monitoring. The framework relocates computational complexity from the edge devices to the core, ensuring that resource-constrained IoT devices are not burdened with intensive processing tasks.
  arXiv  Detail & Related papers  (2025-02-07T14:00:04Z)
• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• Reducing Data Bottlenecks in Distributed, Heterogeneous Neural Networks [5.32129361961937]
  This paper investigates the impact of bottleneck size on the performance of deep learning models in embedded multicore and many-core systems. We apply a hardware-software co-design methodology where data bottlenecks are replaced with extremely narrow layers to reduce the amount of data traffic. Hardware-side evaluation reveals that higher bottleneck ratios lead to substantial reductions in data transfer volume across the layers of the neural network.
  arXiv  Detail & Related papers  (2024-10-12T21:07:55Z)
• Speech Emotion Recognition under Resource Constraints with Data Distillation [64.36799373890916]
  Speech emotion recognition (SER) plays a crucial role in human-computer interaction. The emergence of edge devices in the Internet of Things presents challenges in constructing intricate deep learning models. We propose a data distillation framework to facilitate efficient development of SER models in IoT applications.
  arXiv  Detail & Related papers  (2024-06-21T13:10:46Z)
• A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission [10.174575604689391]
  We propose a novel sensing module to equip sensing frameworks with intelligent data transmission capabilities. We integrate a highly efficient machine learning model placed near the sensor. This model provides prompt feedback for the sensing system to transmit only valuable data while discarding irrelevant information.
  arXiv  Detail & Related papers  (2024-02-03T05:41:39Z)
• Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things [4.68267059122563]
  We present an innovative Edge-assisted U-Shaped Split Federated Learning (EUSFL) framework, which harnesses the high-performance capabilities of edge servers. In this framework, we leverage Federated Learning (FL) to enable data holders to collaboratively train models without sharing their data. We also propose a novel noise mechanism called LabelDP to ensure that data features and labels can securely resist reconstruction attacks.
  arXiv  Detail & Related papers  (2023-11-08T05:14:41Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• Remote Multilinear Compressive Learning with Adaptive Compression [107.87219371697063]
  MultiIoT Compressive Learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. We propose a novel optimization scheme that enables such a feature for MCL models.
  arXiv  Detail & Related papers  (2021-09-02T19:24:03Z)
• Differentially Private Federated Learning for Resource-Constrained Internet of Things [24.58409432248375]
  Federated learning is capable of analyzing the large amount of data from a distributed set of smart devices without requiring them to upload their data to a central place. This paper proposes a novel federated learning framework called DP-PASGD for training a machine learning model efficiently from the data stored across resource-constrained smart devices in IoT.
  arXiv  Detail & Related papers  (2020-03-28T04:32:54Z)

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.