Abstract
Multiagent systems promote a decentralized and distributed approach that enable the division of complex problems into smaller parts. The use of multiagent systems also enables the representation of physical entities, such as persons, pursuing their own goals in an active and proactive society. Currently developments are promoting the idea of having machine learning models in agents to enable intelligent decisions in agents-side. However, machine learning, required assess to large datasets that cannot be available locally to individual agents, demanding the sharing of data or the use of public available datasets to training models for a given agent. To address this issue, this paper proposes the use of federated learning to enable the existence of a collaborative learning model that respects the data privacy, security, and ownership and can be in compliance with the European General Data Protection Regulation (EU GDPR). This paper proposes a novel framework called Python-based framework for agent-based communities powered by federated learning (PEAK FL) that will provide all the necessary tools to build powerful federated learning solutions based on agent communities. This framework provides the users the ability to implement and test hybrid solutions (multiagent-based federated learning systems) in a simple-to-use way, removing the unnecessary boilerplates.
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Acknowledgements
This article is a result of the project RETINA (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.
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Ribeiro, B., Gomes, L., Barbarroxa, R., Vale, Z. (2023). A Novel Framework for Multiagent Knowledge-Based Federated Learning Systems. In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Lecture Notes in Computer Science(), vol 13955. Springer, Cham. https://doi.org/10.1007/978-3-031-37616-0_25
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