A Multi-Agent System Empowered by Federated Learning and Genetic Programming

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@InProceedings{Gomes:2023:SIU,

• author = "Luis Gomes and Bruno Ribeiro and Fernando Lezama and Zita Vale",
• booktitle = "2023 31st Signal Processing and Communications Applications Conference (SIU)",
• title = "A Multi-Agent System Empowered by Federated Learning and Genetic Programming",
• year = "2023",
• abstract = "The use of multi-agent systems enables the modelling of complex and decentralized solutions, giving the ability to have agents representing different entities and assets in a social environment where they can interact and pursue their individual goals. However, multi-agent systems are usually data-driven solutions in which interactions are performed based on data sharing and environmental feedback. Therefore, the integration of multi-agent systems with federated learning, a knowledge-driven approach, allows agents to share knowledge among them in a collaborative and cooperative approach. This integration can be well seen in decentralized solutions where similar entities can benefit from collaborative and cooperative environments. This is the case in industrial environments and in smart grid environments, namely for the improvement of learning models. This paper proposes a methodology composed of a multi-agent system where the agents are empowered by federated learning. The proposed methodology was tested and validated using a genetic programming model with MNIST dataset in terms of feasibility and performance.",
• keywords = "genetic algorithms, genetic programming, Data privacy, Federated learning, Collaboration, Signal processing, Smart grids, Multi-agent systems, data-driven, federated learning, knowledge-driven, multi-agent system",
• DOI = "doi:10.1109/SIU59756.2023.10223778",
• ISSN = "2165-0608",
• month = jul,
• notes = "Also known as \cite{10223778}",

}

Genetic Programming entries for Luis Gomes Bruno Ribeiro Fernando Lezama Zita Vale

Citations