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Creators/Authors contains: "Jiacong Xu, Riley Kilfoyle"

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  1. (, 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC))
    Machine learning has shown incredibly potential in many fields of application ranging from ChatGPT and Bard to Tesla’s autonomous vehicles. These ML models require vast amounts of data and communications overhead in order to be effective. In this paper we propose a communication-efficient time series forecasting model combining the most recent advancements in MetaFormer architecture implemented across a federated series of learning nodes. The time series prediction performance and communication overhead cost of the distributed model is compared against a similar centralized model and shown to have parity in performance while consuming much lower data rates during training. 
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    Accepted Manuscript Full Text Available