Hussaini, MortesaStein, AnthonyKlein, MaikeKrupka, DanielWinter, CorneliaGergeleit, MartinMartin, Ludger2024-10-212024-10-212024978-3-88579-746-32944-7682https://dl.gi.de/handle/20.500.12116/45084Securing food supply and protecting the environment at the same time are two of the major challenges modern agriculture is facing. Research into Artificial Intelligence has seen an enormous increase in interest and potential fields of application. In agriculture, farmers can benefit enormously from this technology. However, the diverse potential is also contrasted by a number of socio-economic concerns and challenges. This aspect needs to be addressed in order to foster a trustworthy use and proliferation of AI applications in socio-technical systems, such as digital agriculture. Federated Learning is a ML paradigm that constitutes a promising solution to this challenge. However, in agricultural use cases exhibiting high degrees of data heterogeneity, client-drift can occur and needs to be addressed. In this concept paper, we propose the advancement of Personalised Federated Learning with multiple levels of sub-federations as a potential solution.enArtificial IntelligenceClient-DriftData SovereigntyDigital AgricultureDistributed Machine LearningPersonalised Federated LearningText/Conference Paper10.18420/inf2024_1121617-54682944-7682