The pressing need for sustainable development solutions necessitates innovative data-
driven tools. Machine learning (ML) offers significant potential, but faces challenges in …

Federated Learning (FL), a burgeoning approach in machine learning, facilitates
collaborative model training across distributed devices while maintaining data privacy …

Federated learning (FL) has emerged as a promising paradigm that enables clients to
collaboratively train a shared global model without uploading their local data. To alleviate …

In the rapidly advancing landscape of machine learning, Federated Learning (FL) stands as
a transformative paradigm, preserving data privacy and overcoming challenges in training …

Federated learning (FL) provides a promising collaborative framework to build a model from
distributed clients, and this work investigates the carbon emission of the FL process. Cloud …

This research delves into the consequences of the high complexity of on-device operations
executed during the federated learning process. We investigate how the varying …

Federated learning (FL) enabled creating models that are competitive to centralized
machine learning models, without compromising user privacy. Participating FL clients train …

Despite impressive results, deep learning-based technologies also raise severe privacy and
environmental concerns induced by the training procedure often conducted in data centers …

Federated learning (FL) is a promising collaborative learning approach in edge computing,
reducing communication costs and addressing the data privacy concerns of traditional cloud …

Federated learning (FL) is a promising technique for collaboratively training machine-
learning models on massively distributed clients data under privacy constraints. However …