Towards Energy Efficient Distributed Federated Learning for 6G Networks

• URL: http://arxiv.org/abs/2201.08270v1
• Date: Wed, 19 Jan 2022 06:37:57 GMT
• Title: Towards Energy Efficient Distributed Federated Learning for 6G Networks
• Authors: Sunder Ali Khowaja, Kapal Dev, Parus Khuwaja, Paolo Bellavista
• Abstract summary: IoT/edge devices need to transmit the data directly to the base station for training the model using machine learning techniques. Data transmission introduces privacy issues that might lead to security concerns and monetary losses. We propose distributed federated learning (DBFL) framework that overcomes the connectivity and energy efficiency issues for distant devices.
• Score: 9.386341375741225
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The provision of communication services via portable and mobile devices, such as aerial base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally, IoT/edge devices need to transmit the data directly to the base station for training the model using machine learning techniques. The data transmission introduces privacy issues that might lead to security concerns and monetary losses. Recently, Federated learning was proposed to partially solve privacy issues via model-sharing with base station. However, the centralized nature of federated learning only allow the devices within the vicinity of base stations to share the trained models. Furthermore, the long-range communication compels the devices to increase transmission power, which raises the energy efficiency concerns. In this work, we propose distributed federated learning (DBFL) framework that overcomes the connectivity and energy efficiency issues for distant devices. The DBFL framework is compatible with mobile edge computing architecture that connects the devices in a distributed manner using clustering protocols. Experimental results show that the framework increases the classification performance by 7.4\% in comparison to conventional federated learning while reducing the energy consumption.

Related papers

• Sparse Federated Training of Object Detection in the Internet of Vehicles [13.864554148921826]
  Object detection is one of the key technologies in the Internet of Vehicles (IoV) Current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server. We propose a federated learning-based framework, where well-trained local models are shared in the central server.
  arXiv  Detail & Related papers  (2023-09-07T08:58:41Z)
• Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks [8.21384946488751]
  Unmanned aerial vehicles (UAV) play many roles in a modern smart city such as the delivery of goods, mapping real-time road traffic and monitoring pollution. Traditional machine learning models for drones encounter data privacy problems, communication costs and energy limitations. We propose two aggregation methods: Commutative FL and Alternate FL, based on the existing architecture of decentralised Federated Learning for UAV Networks (DFL-UN)
  arXiv  Detail & Related papers  (2023-04-13T14:00:34Z)
• A Practical Cross-Device Federated Learning Framework over 5G Networks [47.72735882790756]
  The concept of federated learning (FL) was first proposed by Google in 2016. We propose a novel cross-device federated learning framework using anonymous communication technology and ring signature. In addition, our scheme implements a contribution-based incentive mechanism to encourage mobile users to participate in FL.
  arXiv  Detail & Related papers  (2022-04-18T02:31:06Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer [42.30741737568212]
  We propose the solution of powering devices using wireless power transfer (WPT) This work aims at the derivation of guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system. The results provide useful guidelines on WPT provisioning to provide a guaranteer on learning performance.
  arXiv  Detail & Related papers  (2021-02-24T15:47:34Z)
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices [78.38046945665538]
  federated learning (FL) over massive mobile edge devices opens new horizons for numerous intelligent mobile applications. FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training. We develop a convergence-guaranteed FL algorithm enabling flexible communication compression.
  arXiv  Detail & Related papers  (2020-12-22T02:54:18Z)
• To Talk or to Work: Energy Efficient Federated Learning over Mobile Devices via the Weight Quantization and 5G Transmission Co-Design [49.95746344960136]
  Federated learning (FL) is a new paradigm for large-scale learning tasks across mobile devices. It is not clear how to establish an effective wireless network architecture to support FL over mobile devices. We develop a wireless transmission and weight quantization co-design for energy efficient FL over heterogeneous 5G mobile devices.
  arXiv  Detail & Related papers  (2020-12-21T01:13:44Z)
• Wireless Communications for Collaborative Federated Learning [160.82696473996566]
  Internet of Things (IoT) devices may not be able to transmit their collected data to a central controller for training machine learning models. Google's seminal FL algorithm requires all devices to be directly connected with a central controller. This paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller.
  arXiv  Detail & Related papers  (2020-06-03T20:00:02Z)
• Communication Efficient Federated Learning with Energy Awareness over Wireless Networks [51.645564534597625]
  In federated learning (FL), the parameter server and the mobile devices share the training parameters over wireless links. We adopt the idea of SignSGD in which only the signs of the gradients are exchanged. Two optimization problems are formulated and solved, which optimize the learning performance. Considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a sign-based algorithm is proposed.
  arXiv  Detail & Related papers  (2020-04-15T21:25:13Z)
• Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks [8.456633924613456]
  Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems. The paper proposes a fully distributed (or server-less) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing.
  arXiv  Detail & Related papers  (2019-12-27T15:16:04Z)

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.