In this paper, we propose a federated learning framework using a mixture of experts to balance the specialist nature of a locally trained model with the generalist knowledge of a global model in a federated learning setting. Our results show that the mixture of experts model is better suited ...
Federated learning (FL) is an emerging distributed machine learning paradigm that avoids data sharing among training nodes so as to protect data privacy. Under the coordination of the FL server, each client conducts model training using its own computing resource and private data set. The global ...
By facilitating the clients to download multiple pre-aggregated prompts as fixed non-local experts, we propose Personalized Federated Mixture of Adaptive Prompts (pFedMoAP), a novel FL framework that personalizes the prompt learning process through the lens of Mixture of Experts (MoE). pFedMoAP ...
They have been able to impart insights, especially using hybrid artificial neural networks and evolutionary algorithms. Voyant et al. [31] summarise all the methods of solar irradiation forecasting using machine learning approaches. The study of Akhtar et al. [32] provides a systematic and critical...
This AI Paper Proposes FLORA: A Novel Machine Learning Approach that Leverages Federated Learning and Parameter-Efficient Adapters to Train Visual-Language Models VLMs
using artificial intelligence [2] to materialize a variety of healthcare applications which includes distant monitoring of patient and prognosis of diseases. Deep Learning (DL) approaches, for example, have shown potential in biomedical image analysis for earlier identification of acute illnesses by ...
The concept of distributing features across nodes and then federating the profile’s results into a final one is analogous to that of ensemble learning or a mixture of experts, and produces more reliable results than a single expert. In summary, the experimental evaluation showcased the ...
(FL), which enables the use of a set of physically distributed data repositories, usually known as nodes, satisfying the restriction that the data do not leave the repository. Under these conditions, FL can build high-quality, accurate deep-learning models using a lot of available data ...