Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices

• URL: http://arxiv.org/abs/2403.09141v1
• Date: Thu, 14 Mar 2024 07:40:32 GMT
• Title: Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices
• Authors: Gleb Radchenko, Victoria Andrea Fill,
• Abstract summary: Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their computational capabilities, emphasizing the practicality of edge AI. Our study explores methods that enable distributed data processing through AI-enabled edge devices, enhancing collaborative learning capabilities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Initially considered as low-power units with limited autonomous processing, Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their computational capabilities, emphasizing the practicality of edge AI. Such progress introduces new challenges of optimizing AI tasks for the limitations of energy and network resources typical in Edge computing environments. Our study explores methods that enable distributed data processing through AI-enabled edge devices, enhancing collaborative learning capabilities. A key focus of our research is the challenge of determining confidence levels in learning outcomes, considering the spatial and temporal variability of data sets encountered by independent agents. To address this issue, we investigate the application of Bayesian neural networks, proposing a novel approach to manage uncertainty in distributed learning environments.

Related papers

• Edge AI Collaborative Learning: Bayesian Approaches to Uncertainty Estimation [0.0]
  We focus on determining confidence levels in learning outcomes considering the spatial variability of data encountered by independent agents. We implement a 3D environment simulation using the Webots platform to simulate collaborative mapping tasks. Experiments demonstrate that BNNs can effectively support uncertainty estimation in a distributed learning context.
  arXiv  Detail & Related papers  (2024-10-11T09:20:16Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Uncertainty Estimation in Multi-Agent Distributed Learning [0.0]
  KDT NEUROKIT2E project aims to establish a new open-source framework to facilitate AI applications on edge devices. Our research focuses on the mechanisms and methodologies that allow edge network-enabled agents to engage in collaborative learning in distributed environments.
  arXiv  Detail & Related papers  (2023-11-22T12:48:20Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures [7.085772863979686]
  Deep neural networks (DNNs) have led to large strides in various Artificial Intelligence (AI) inference tasks, such as object and speech recognition. deploying such AI models across commodity devices faces significant challenges. We present techniques for achieving real-time performance following a cross-stack approach.
  arXiv  Detail & Related papers  (2021-06-21T11:23:12Z)
• Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence [45.076180487387575]
  Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
  arXiv  Detail & Related papers  (2021-05-04T23:42:06Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.