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Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users. While successful, FL research is currently limited by the difficulties of ...

Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with privacy by default. However, despite the remarkable advancement, this ...

Federated learning (FL) has recently attracted much attention due to its advantages for data privacy. But every coin has two sides: protecting users' data (not requiring users to send their data) also makes FL more vulnerable to some types of attacks, ...

Most Federated Learning (FL) algorithms proposed to date obtain the global model by aggregating multiple local models that typically share the same architecture, thus overlooking the impact on the hardware heterogeneity of edge devices. To address this ...

Mobile devices and the immense amount and variety of data they generate are key enablers of machine learning (ML)-based applications. Traditional ML techniques have shifted toward new paradigms such as federated learning (FL) and split learning (SL) to ...