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Hace 2 días · Federated learning, as a distributed machine learning framework, aims to protect data privacy while addressing the issue of data silos by collaboratively training models across multiple clients. However, a significant challenge to federated learning arises from the non-independent and identically distributed (non-iid) nature of data across different clients. non-iid data can lead to ...
Hace 2 días · But we need to keep in mind that FedAvg is at the core of many federated machine learning algorithms. Data testing was conducted using the Flower and Kaggle platforms with the above algorithms. Federated machine learning technology is usable in smartphones and other devices where it can create accurate predictions without revealing raw personal ...
Hace 3 días · When optimizing machine learning models, there are various scenarios where gradient computations are challenging or even infeasible. Furthermore, in reinforcement learning (RL), preference-based RL that only compares between options has wide applications, including reinforcement learning with human feedback in large language models. In this paper, we systematically study optimization of a ...
Hace 4 días · 1 Understanding Metrics. To evaluate ML algorithms effectively, you must first familiarize yourself with the various performance metrics available. For classification problems, accuracy, precision ...
Hace 5 días · Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and ...
Hace 2 días · Addressing the pervasive issue of school-dropout in Egypt is imperative for advancing the country's educational system and fostering its social and economic progress. Recently, there is a growing interest in leveraging Machine Learning techniques as proactive tools for identifying students at-risk of dropping out so as to carry out timely interventions. This study implements nine supervised ...
Hace 4 días · However, few studies have explored the use of the Bagging algorithm in this field. Therefore, this study proposes a classification prediction method for student achievement based on the Bagging-CART algorithm. Initially, the student achievement data is preprocessed, and the Apriori method is applied to mine the strongly associated dataset.
