Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks

Yong Xiao, Yingyu Li, Guangming Shi, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper studies an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) based on the datasets uploaded from a multi-technology-supported IoT network. The data uploading performance of IoT network and the computational capacity of edge servers are entangled with each other in influencing the FL model training process. 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 as well as their local data processing capacity and only request the amount of data that is sufficient for training a satisfactory model. We evaluate the energy cost for data uploading when two widely-used IoT solutions: licensed band IoT (e.g., 5G NB-IoT) and unlicensed band IoT (e.g., Wi-Fi, ZigBee, and 5G NR-U) are available to each IoT device. We prove that the cost minimization problem of the entire IoT network is separable and can be divided into a set of subproblems, each of which can be solved by an individual edge server. We also introduce a mapping function to quantify the computational load of edge servers under different combinations of three key parameters: size of the dataset, local batch size, and number of local training passes. Finally, we adopt an Alternative Direction Method of Multipliers (ADMM)-based approach to jointly optimize energy cost of the IoT network and average computing resource utilization of edge servers. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network. Simulation results show that our proposed framework significantly improves the resource efficiency of the IoT network and edge servers with only a limited sacrifice on the model convergence performance.

Original languageEnglish (US)
Title of host publication12th International Conference on Wireless Communications and Signal Processing, WCSP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages86-92
Number of pages7
ISBN (Electronic)9781728172361
DOIs
StatePublished - Oct 21 2020
Event12th International Conference on Wireless Communications and Signal Processing, WCSP 2020 - Nanjing, China
Duration: Oct 21 2020Oct 23 2020

Publication series

Name12th International Conference on Wireless Communications and Signal Processing, WCSP 2020

Conference

Conference12th International Conference on Wireless Communications and Signal Processing, WCSP 2020
CountryChina
CityNanjing
Period10/21/2010/23/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Keywords

  • 6G
  • IoT
  • edge intelligence
  • federated learning

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