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Federated learning with multichannel ALOHA
journal contributionposted on 2020-04-01, 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.
JournalIEEE wireless communications letters
Pagination499 - 502
Publication classificationC1 Refereed article in a scholarly journal