CoDeFeL WP

Browse all posts in the CoDeFeL WP category.

4 total

01OCT 2026
CoDeFeL
CoDeFeL • WP4
WP4 – Modelling through Control and Machine Learning Control theory provides a principled framework for regulating dynamical systems, while Machine Learning enables complex patterns and predictive models to be extracted from data. By integrating these two fields, we develop efficient, robust, and adaptive methodologies that combine data-driven learning with feedback mechanisms. This synergy provides a […]WP4 – Modelling through Control and Machine Learning Control theory provides a principled framework for regulating dynamical systems, while Machine Learning enables complex patterns and predictive models to be extracted from data. By integrating these two fields, we develop efficient, robust, and adaptive methodologies that combine data-driven learning with feedback mechanisms. This synergy provides a […]
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01OCT 2026
CoDeFeL
CoDeFeL • WP3
WP3 – Federated Learning Federated Learning (FL) is a decentralized Machine Learning paradigm that enables multiple devices or agents to collaboratively train models without directly sharing their local data. This approach is particularly relevant in privacy-sensitive domains such as healthcare and finance, where data sharing is often constrained. Despite its rapid development, a substantial gap […]WP3 – Federated Learning Federated Learning (FL) is a decentralized Machine Learning paradigm that enables multiple devices or agents to collaboratively train models without directly sharing their local data. This approach is particularly relevant in privacy-sensitive domains such as healthcare and finance, where data sharing is often constrained. Despite its rapid development, a substantial gap […]
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01OCT 2026
CoDeFeL
CoDeFeL • WP2
WP2 – Complexity of ResNet Dynamics Residual Neural Networks (ResNets) leverage shortcut connections to facilitate gradient propagation, enabling faster convergence and enhanced robustness. Their residual structure establishes a natural connection with time-discrete dynamical systems, revealing distinctive properties that can be studied and exploited through control-theoretic tools. Building on simultaneous control theory and universal approximation principles, […]WP2 – Complexity of ResNet Dynamics Residual Neural Networks (ResNets) leverage shortcut connections to facilitate gradient propagation, enabling faster convergence and enhanced robustness. Their residual structure establishes a natural connection with time-discrete dynamical systems, revealing distinctive properties that can be studied and exploited through control-theoretic tools. Building on simultaneous control theory and universal approximation principles, […]
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01OCT 2026
CoDeFeL
CoDeFeL • WP1
WP1 – Asymptotics and Turnpike for Deep Neural Networks Turnpike theory, originally an economic concept, describes how optimal trajectories remain close to a stable, efficient regime — the “turnpike” — for most of their evolution, departing from it primarily to accommodate initial and terminal conditions. In Machine Learning, this principle provides a powerful framework for […]
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