Algoritmos de Machine Learning (ML) possibilitam processar um conjunto de dados de
entradas para gerar coeficientes que ajustem a saída a um resultado previamente …

Machine Learning (ML) algorithms process input data making it possible to recognize and
extract patterns from a large data volume. Likewise, Internet of Things (IoT) devices provide …

Federated Learning relies heavily on the data available at the worker nodes. In a majority of
practical use-cases, the data at the worker nodes are Non-IID (non-independent-and …

In this paper, we identify a new phenomenon called activation-divergence which occurs in
Federated Learning (FL) due to data heterogeneity (ie, data being non-IID) across multiple …

Federated Learning allows training of data stored in distributed devices without the need for
centralizing training data, thereby maintaining data privacy. Addressing the ability to handle …

Though successful, federated learning (FL) presents new challenges for machine learning,
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …

Federated learning (FL) is a framework to train machine learning models using
decentralized data, especially unbalanced and non-iid. Adaptive methods can be used to …

Federated learning is a decentralized topology of deep learning, that trains a shared model
through data distributed among each client (like mobile phones, wearable devices), in order …

Federated Learning (FL) is a strategy for training distributed learning models. This approach
gives rise to significant challenges including the non-independent and identically distributed …

Though successful, federated learning presents new challenges for machine learning,
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …