Artificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information. However, conventional FL methods struggle to handle situations where data samples have diverse features and sizes. We propose a Hybrid Federated Learning solution with label synchronization to overcome this challenge. Our FedLabSync algorithm trains a feed-forward Artificial Neural Network while alerts that it can aggregate knowledge of other ML architectures compatible with the Stochastic Gradient Descent algorithm by conducting a penalized collaborative optimization. We conducted two industrial case studies: product inspection in Bosch factories and aircraft component Remaining Useful Life predictions. Our experiments on decentralized data scenarios demonstrate that FedLabSync can produce a global AI model that achieves results on par with those of centralized learning methods.

This paper proposes using reinforcement learning (RL) to schedule maintenance tasks, which can significantly reduce direct operating costs for airlines. The approach consists of a static algorithm for long-term scheduling and an adaptive algorithm for rescheduling based on new maintenance information. To assess the performance of both approaches, three key performance indicators (KPIs) are defined: Ground Time, representing the hours an aircraft spends on the ground; Time Slack, measuring the proximity of tasks to their due dates; and Change Score, quantifying the similarity level between initial and adapted maintenance plans when new information surfaces. The results demonstrate the efficacy of RL in producing efficient maintenance plans, with the algorithms complementing each other to form a solid foundation for routine tasks and real-time responsiveness to new information. While the static algorithm performs slightly better in terms of Ground Time and Time Slack, the adaptive algorithm excels overwhelmingly in terms of Change Score, offering greater flexibility in handling new maintenance information. The proposed RL-based approach can improve the efficiency of aircraft maintenance and has the potential for further research in this area.

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.