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Federated learning with multichannel ALOHA

journal contribution
posted on 01.04.2020, 00:00 authored by Jinho ChoiJinho Choi, Shiva PokhrelShiva Pokhrel
In this letter, we study federated learning in a cellular system with a base station (BS) and a large number of users with local data sets. We show that multichannel random access can provide a better performance than sequential polling when some users are unable to compute local updates (due to other tasks) or in dormant state. In addition, for better aggregation in federated learning, the access probabilities of users can be optimized for given local updates. To this end, we formulate an optimization problem and show that a distributed approach can be used within federated learning to adaptively decide the access probabilities.

History

Journal

IEEE wireless communications letters

Volume

9

Issue

4

Pagination

499 - 502

Publisher

IEEE

Location

Piscataway, N.J.

ISSN

2162-2337

eISSN

2162-2345

Language

eng

Publication classification

C1 Refereed article in a scholarly journal