Secure Federated Learning by Power Control for Internet of Drones

Jingjing Yao, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

Abstract

Fog-aided internet of drones (IoD), where massive training data are collected by drones and analyzed in the fog node, can leverage machine learning to provision various services. Aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage. Federated learning (FL) is hence proposed to preserve drone data privacy by performing local training in drones and sharing training model parameters in the fog node without uploading drone raw data. However, drone privacy can still be divulged to ground eavesdroppers by wiretapping and analyzing uploaded parameters during the FL training process. In this paper, we investigate the power control of all drones to maximize the FL system security rate constrained by drone battery capacities and the quality of service (QoS) requirement (i.e., FL training time). We formulate this problem as a non-linear programming problem and design an algorithm to obtain the optimum solutions with a low computational complexity. Extensive simulations are conducted to demonstrate the performance of our proposed algorithm.

Original languageEnglish (US)
JournalIEEE Transactions on Cognitive Communications and Networking
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

Keywords

  • Communication system security
  • Data models
  • Drones
  • Federated learning
  • Internet of Drones (IoD)
  • Power control
  • Security
  • Training
  • Wireless communication
  • energy consumption
  • fog computing
  • power control
  • quality of service (QoS).
  • security

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