Jan 13, 2024 · This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks.
Dec 24, 2023 · Hence, realizing efficient communication and minimizing transmission delay is essential for distributed algorithms in large-scale IoT networks.
This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks.
This article proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks.
This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks. This ...
By minimizing QE while maintaining consistent TO probability, FedTOE addresses the challenges posed by practical systems with lim-ited radio resources, ...
Aug 7, 2024 · Decentralized Federated Learning with Asynchronous Parameter Sharing for Large-scale IoT Networks. CoRR abs/2401.07122 (2024); 2023. [j2]. view.
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TinyML/DL is a new subfield of ML that allows for the deployment of ML algorithms on low-power devices to process their own data.
This article contributes SAFI, a semi-asynchronous FL algorithm for IoT, allowing FL to be a feasible solution for processing large-scale IoT data when the edge ...