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Abstract: Tiny machine learning (TinyML) represents an emerging research direction that aims to realize machine learning on Internet of Things (IoT) devices ...
We propose a tiny federated learning algorithm, called. TFL-BC, to enable the learning of Bayesian classifiers based on distributed tiny data storage. In TFL-BC ...
This paper proposes a tiny federated learning algorithm for enabling learning of Bayesian classifiers based on distributed tiny data storage, referred to as ...
Tiny Federated Learning with Bayesian Classifiers | Request PDF
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This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning combined with Markov Chain Monte Carlo (MCMC) methods.
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Oct 20, 2024 · Clustered federated learning is a class of FL methods that groups clients that observe similarly distributed data into clusters, such that every ...
Oct 18, 2024 · We evaluate LR-BPFL across a variety of datasets, demonstrating its advantages in terms of calibration, accuracy, as well as computational and ...
A recent work proposed a tiny FL algorithm to enable the learning of Bayesian classifiers based on distributed tiny data storage. It first performs in parallel ...
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Abstract. Federated learning faces huge challenges from model overfitting due to the lack of data and sta- tistical diversity among clients.
Feb 10, 2024 · Federated learning aims to infer a shared model from private and decentralized data stored locally by multiple clients.