… A technique to distributed learning based on many criteria is … Goal Programming
technique in its Chebyshev formulation, which is a variation of the Weighted Goal Programming …

… However, not all Federated Learning approaches are privacy … Federated Learning framework
is suitable for cancer detection. To this end, we tested the Federated Goal Programming …

… However, by sharing the gradient, Federated Learning approaches fail to comply with … a
Goal Programming approach to address this issue. In particular, we define the federated model …

… However, our goal was to directly compare the performance of two pairs of systems, so it
seemed more appropriate to generate an artificial data set. Our data set is based on the …

… For energy efficient FL over wireless networks, [20] studied a joint learning and communication
problem with the goal of minimizing the total energy consumption of the system under a …

… to the participants to achieve the learning goal. To provide a comprehensive survey and
facilitate the potential research of this area, we systematically introduce the existing works of …

Federated learning has emerged as a promising, massively distributed way to train a joint …
against malicious adversaries, we formulate federated learning as multi-objective optimization …

… Our goal is to learn a model h which 1) satisfies MCF as we defined in Definition 3.1; 2)
maintains consistent performances across all clients. We will use ∆DPk defined in Eq.(2) as …

… Based on the closed-form theoretical results, we formulate a joint optimization problem of
learning, worker selection, and power control, with a goal of minimizing the global FL loss …

… in federated learning. This setting can easily be expanded to a multitask learning system in
order to manage real-world federated … A long term goal pursued by research communities (in…