A comparison of consensus strategies for distributed learning of random vector functional-link networks

Distributed machine learning is the problem of inferring a desired relation when the training data is distributed throughout a network of agents (e.g. robots in a robot swarm). Multiple families of distributed learning algorithms are based on the decentralized average consensus (DAC) protocol, an efficient algorithm for computing an average starting from local measurement vectors. The performance of DAC, however, is strongly dependent on the choice of a weighting matrix associated to the network. In this paper, we perform a comparative analysis of the relative performance of 4 different strategies for choosing the weighting matrix. As an applicative example, we consider the distributed sequential algorithm for Random Vector Functional-Link networks. As expected, our experimental simulations show that the training time required by the algorithm is drastically reduced when considering a proper initialization of the weights.