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distributed optimization

topic2 events
papersSEP 12 04:00 UTC

Model-based reinforcement learning with inverse models controls modular production systems

A new arXiv paper proposes a framework for data-driven self-learning control of highly flexible, modular manufacturing systems. The approach combines model-based reinforcement learning with approximate inverse process models to improve distributed optimization. The work targets industrial settings where production modules can be reconfigured.

reinforcement-learning/manufacturing/arxiv/optimization
papersSEP 10 04:00 UTC

arXiv Paper Introduces High-Order Regularization for Federated Learning

A new arXiv preprint addresses a problem in federated learning where clients that run multiple local optimization steps produce parameter updates of very different scales. The authors observe that the proximal term used by FedProx exerts a restraining force that scales only linearly with update size, giving it weak leverage over large displacements, and they propose a higher-order regularization scheme as a stronger alternative.

federated-learning/regularization/optimization/machine-learning