A collaborative learning framework via federated meta-learning

Sen Lin, Guang Yang, Junshan Zhang

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

Abstract

—Many IoT applications at the network edge demand intelligent decisions in a real-time manner. The edge device alone, however, often cannot achieve real-time edge intelligence due to its constrained computing resources and limited local data. To tackle these challenges, we propose a platform-aided collaborative learning framework where a model is first trained across a set of source edge nodes by a federated meta-learning approach, and then it is rapidly adapted to learn a new task at the target edge node, using a few samples only. Further, we investigate the convergence of the proposed federated meta-learning algorithm under mild conditions on node similarity and the adaptation performance at the target edge. To combat against the vulnerability of meta-learning algorithms to possible adversarial attacks, we further propose a robust version of the federated meta-learning algorithm based on distributionally robust optimization, and establish its convergence under mild conditions. Experiments on different datasets demonstrate the effectiveness of the proposed Federated Meta-Learning based framework.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 40th International Conference on Distributed Computing Systems, ICDCS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages289-299
Number of pages11
ISBN (Electronic)9781728170022
DOIs
StatePublished - Nov 2020
Event40th IEEE International Conference on Distributed Computing Systems, ICDCS 2020 - Singapore, Singapore
Duration: Nov 29 2020Dec 1 2020

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2020-November

Conference

Conference40th IEEE International Conference on Distributed Computing Systems, ICDCS 2020
Country/TerritorySingapore
CitySingapore
Period11/29/2012/1/20

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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