3 accepted papers
Federated learning (FL) is now recognized as a key framework for communication-efficient collaborative learning. Most theoretical and empirical studies, however, rely on the assumption that clients have access to pre-collected data sets, with limited investigation into scenarios where clients contin…
In predictive maintenance an important problem is to optimize the quantity of information to be transmitted at the control center to guarantee reliable fault detection while limiting sensor power consumption. This problem relies directly on the sensor configurations (e.g., sampling rate, coding, qua…
Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for nonsmooth and nonconvex objectives, few convergence guarantees exist beyond cases where closed-form proximal operator solutio…