  Decentralized and federated learning algorithms face data heterogeneity as
one of the biggest challenges, especially when users want to learn a specific
task. Even when personalized headers are used concatenated to a shared network
(PF-MTL), aggregating all the networks with a decentralized algorithm can
result in performance degradation as a result of heterogeneity in the data. Our
algorithm uses exchanged gradients to calculate the correlations among tasks
automatically, and dynamically adjusts the communication graph to connect
mutually beneficial tasks and isolate those that may negatively impact each
other. This algorithm improves the learning performance and leads to faster
convergence compared to the case where all clients are connected to each other
regardless of their correlations. We conduct experiments on a synthetic
Gaussian dataset and a large-scale celebrity attributes (CelebA) dataset. The
experiment with the synthetic data illustrates that our proposed method is
capable of detecting tasks that are positively and negatively correlated.
Moreover, the results of the experiments with CelebA demonstrate that the
proposed method may produce significantly faster training results than
fully-connected networks.
