  Federated Edge Learning (FEEL) is a distributed machine learning technique
where each device contributes to training a global inference model by
independently performing local computations with their data. More recently,
FEEL has been merged with over-the-air computation (OAC), where the global
model is calculated over the air by leveraging the superposition of analog
signals. However, when implementing FEEL with OAC, there is the challenge on
how to precode the analog signals to overcome any time misalignment at the
receiver. In this work, we propose a novel synchronization-free method to
recover the parameters of the global model over the air without requiring any
prior information about the time misalignments. For that, we construct a convex
optimization based on the norm minimization problem to directly recover the
global model by solving a convex semi-definite program. The performance of the
proposed method is evaluated in terms of accuracy and convergence via numerical
experiments. We show that our proposed algorithm is close to the ideal
synchronized scenario by $10\%$, and performs $4\times$ better than the simple
case where no recovering method is used.
