An Optimal Stopping Approach for Iterative Training in Federated Learning

Pengfei Jiang, Lei Ying

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

Abstract

This paper studies the problem of iterative training in Federated Learning. We consider a system with a single parameter server (PS) and M client devices for training a predictive learning model with distributed data sets on the client devices. The clients communicate with the parameter server using a common wireless channel, so each time only one device can transmit. The training is an iterative process consisting of multiple rounds. At beginning of each round (also called an iteration), each client trains the model, broadcast by the parameter server at the beginning of the round, with its own data. After finishing training, the device transmits the update to the parameter server when the wireless channel is available. The server aggregates updates to obtain a new model and broadcasts it to all clients to start a new round. We consider adaptive training where the parameter server decides when to stop/restart a new round, and formulate the problem as an optimal stopping problem. While this optimal stopping problem is difficult to solve, we propose a modified optimal stopping problem. We first develop a low complexity algorithm to solve the modified problem, which also works for the original problem. Experiments on a real data set shows significant improvements compared with policies collecting a fixed number of updates in each round.

Original languageEnglish (US)
Title of host publication2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728140841
DOIs
StatePublished - Mar 2020
Event54th Annual Conference on Information Sciences and Systems, CISS 2020 - Princeton, United States
Duration: Mar 18 2020Mar 20 2020

Publication series

Name2020 54th Annual Conference on Information Sciences and Systems, CISS 2020

Conference

Conference54th Annual Conference on Information Sciences and Systems, CISS 2020
CountryUnited States
CityPrinceton
Period3/18/203/20/20

Keywords

  • Distributed Machine Learning
  • Federated Learning
  • Optimal Stopping

ASJC Scopus subject areas

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

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  • Cite this

    Jiang, P., & Ying, L. (2020). An Optimal Stopping Approach for Iterative Training in Federated Learning. In 2020 54th Annual Conference on Information Sciences and Systems, CISS 2020 [9086230] (2020 54th Annual Conference on Information Sciences and Systems, CISS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS48834.2020.1570616094