New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design

• URL: http://arxiv.org/abs/2008.10805v1
• Date: Tue, 25 Aug 2020 04:08:10 GMT
• Title: New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design
• Authors: Kartikeya Bhardwaj, Wei Chen, Radu Marculescu
• Abstract summary: We highlight three major challenges to large-scale adoption of deep learning at the edge: Hardware-constrained IoT devices, data security and privacy in the IoT era, and lack of network-aware deep learning algorithms for distributed inference across multiple IoT devices. We believe that the above research directions need a network-centric approach to enable the edge intelligence and, therefore, fully exploit the true potential of IoT.
• Score: 20.273836530269577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we first highlight three major challenges to large-scale adoption of deep learning at the edge: (i) Hardware-constrained IoT devices, (ii) Data security and privacy in the IoT era, and (iii) Lack of network-aware deep learning algorithms for distributed inference across multiple IoT devices. We then provide a unified view targeting three research directions that naturally emerge from the above challenges: (1) Federated learning for training deep networks, (2) Data-independent deployment of learning algorithms, and (3) Communication-aware distributed inference. We believe that the above research directions need a network-centric approach to enable the edge intelligence and, therefore, fully exploit the true potential of IoT.

Related papers

• A Systematic Mapping Study on SDN Controllers for Enhancing Security in IoT Networks [0.0]
  We review the current body of knowledge on enhancing the security of IoT networks using SDN controllers. We conclude that the SDN controller architecture commonly used for securing IoT networks is the centralized controller architecture.
  arXiv  Detail & Related papers  (2024-08-02T14:44:15Z)
• A Cutting-Edge Deep Learning Method For Enhancing IoT Security [0.0]
  This paper proposes an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on the CICIDS 2017 dataset, achieved an accuracy of 99.52% in classifying network traffic as either benign or malicious.
  arXiv  Detail & Related papers  (2024-06-18T08:42:51Z)
• Deep Learning Approaches for Network Traffic Classification in the Internet of Things (IoT): A Survey [0.0]
  The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices. Effectively classifying this network traffic is crucial for optimizing resource allocation, enhancing security measures, and ensuring efficient network management in IoT systems. Deep learning has emerged as a powerful technique for network traffic classification due to its ability to automatically learn complex patterns and representations from raw data.
  arXiv  Detail & Related papers  (2024-02-01T14:33:24Z)
• 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)
• Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges [55.82853124625841]
  Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
  arXiv  Detail & Related papers  (2023-09-14T05:43:36Z)
• Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network [0.0]
  Internet of Things (IoT) has altered living by controlling devices/things over the Internet. To bring down the IoT network, attackers can utilise these devices to conduct a variety of network attacks. In this paper, we have developed an unsupervised ensemble learning model that is able to detect new or unknown attacks in an IoT network from an unlabelled dataset.
  arXiv  Detail & Related papers  (2022-07-16T11:12:32Z)
• Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks [17.071452978622123]
  Federated Learning (FL) has recently become an effective approach for cyberattack detection systems. FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks.
  arXiv  Detail & Related papers  (2021-12-02T05:26:29Z)
• Federated Learning: A Signal Processing Perspective [144.63726413692876]
  Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local datasets, without explicitly exchanging the data. This article provides a unified systematic framework for federated learning in a manner that encapsulates and highlights the main challenges that are natural to treat using signal processing tools.
  arXiv  Detail & Related papers  (2021-03-31T15:14:39Z)
• Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning [73.85267769520715]
  Internet of Things (IoT) with its growing number of deployed devices and applications raises significant challenges for network maintenance procedures. We formulate a problem of autonomous maintenance in IoT networks as a Partially Observable Markov Decision Process. We utilize Deep Reinforcement Learning algorithms (DRL) to train agents that decide if a maintenance procedure is in order or not and, in the former case, the proper type of maintenance needed.
  arXiv  Detail & Related papers  (2020-12-31T11:19:51Z)
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks [96.24723959137218]
  We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
  arXiv  Detail & Related papers  (2020-11-25T12:51:59Z)
• 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.