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Federated Learning (FL) represents a promising distributed learning methodology particularly suitable for dynamic and heterogeneous environments characterized by the presence of Internet of Things (IoT) devices and Edge Computing infrastructures. In this context, FL allows you to train machine learning models directly on edge devices, mitigating data privacy concerns and reducing latency due to transmitting data to central servers. However, the heterogeneity of computational resources, the variability of network connections, and the mobility of IoT devices pose significant challenges to the efficient implementation of FL. This work explores advanced techniques for dynamic model adaptation and heterogeneous data management in edge computing scenarios, proposing innovative solutions to improve the robustness and efficiency of federated learning. We present an innovative solution based on Kubernetes which enables the fast application of FL models to Heterogeneous Architectures. Experimental results demonstrate that our proposals can improve the performance of FL in IoT and edge environments, offering new perspectives for the practical implementation of decentralized intelligent systems. [ABSTRACT FROM AUTHOR]

Copyright of Orthodontie Française is the property of John Libbey Eurotext Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Orthodontie Française is the property of John Libbey Eurotext Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper investigates if and how knowledge spills between and within sectors when gender is considered. The data show that females and males are concentrated in different sectors. This different concentration can be a source of different proximity and cognitive distance between gender, and, therefore, the knowledge spillovers can be hindered by this different distribution due to the "gender barriers". Furthermore, they have different skills and capabilities that can impact skill relatedness connectivity. This "gender diversity" can make the labour market stickier and more polarized. Another two dimensions will be considered: sector composition and spatial spillovers. The latter aims to investigate if and how spatial units under consideration (provinces) are affected by their neighbors and vice versa. An SDM (Spatial Durbin Model) will capture the spatial spillovers. The analysis will be carried out at the Italian provincial level over 2012-2017. The results highlight that labour growth can be influenced by gender distribution within and between industries. [ABSTRACT FROM AUTHOR]