Efficient federated multi-view learning

Highlights：

• By regarding multi-view learning as a natural choice to address feature heterogeneity in federated setting, we present a novel federated multi-view learning method based on orthogonal matrix factorization due to its extensibility and lower costs.

• By allowing each node with the flexibility to address its subproblem approximately, the proposed optimization algorithm is able to handle communication variability associated with node heterogeneity.

• We further provide convergence guarantees for the proposed algorith that also take the communication variability into consideration and provide insight into the implementation performance.

• Empirical study shows encouraging results of our model in comparison to several state-of-the-art algorithms, as we illustrate through simulations on benchmark federated multi-view datasets.