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Cette étude de cas porte sur une démarche de coopération Sud-Sud réalisée autour de la visite du centre de formation Songhaï au Bénin. Elle fait ressortir les résultats en matière d’acquisition de nouvelles connaissances portant sur tout le cycle de production agroalimentaire et leur application subséquente par les personnes participantes, tout en identifiant les facteurs de succès et les leçons apprises. L’étude souligne toutefois qu’une plus grande place faite aux échanges entre les pairs durant la visite aurait permis de renforcer les liens et les collaborations entre les partenaires., This case study explores a cooperation initiative that brought together 10 organizations working on food production for a visit to the Songhai Training Center in Benin. It shows effective learning covering all steps of food production and processing and its subsequent application by participating members in their home countries., The case study is part of a series conducted in the final phase of Oxfam-Québec’s Volunteer Cooperation Program, Program ACCES Innovation, which took place in ten countries from 2015 to 2020.

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning maintenance engineers of impending failure or identifying current system health conditions. However, single operators may not have sufficiently large fleets of systems or component units to collect sufficient data to develop data-driven algorithms. Collecting a satisfactory quantity of fault patterns for safety-critical systems is particularly difficult due to the rarity of faults. Federated learning (FL) has emerged as a promising solution to leverage datasets from multiple operators to train a decentralized asset fault diagnosis model while maintaining data confidentiality. However, there are still considerable obstacles to overcome when it comes to optimizing the federation strategy without leaking sensitive data and addressing the issue of client dataset heterogeneity. This is particularly prevalent in fault diagnosis applications due to the high diversity of operating conditions and system configurations. To address these two challenges, we propose a novel clustering-based FL algorithm where clients are clustered for federating based on dataset similarity. To quantify dataset similarity between clients without explicitly sharing data, each client sets aside a local test dataset and evaluates the other clients' model prediction accuracy and uncertainty on this test dataset. Clients are then clustered for FL based on relative prediction accuracy and uncertainty.