  Data-driven evolutionary optimization has witnessed great success in solving
complex real-world optimization problems. However, existing data-driven
optimization algorithms require that all data are centrally stored, which is
not always practical and may be vulnerable to privacy leakage and security
threats if the data must be collected from different devices. To address the
above issue, this paper proposes a federated data-driven evolutionary
optimization framework that is able to perform data driven optimization when
the data is distributed on multiple devices. On the basis of federated
learning, a sorted model aggregation method is developed for aggregating local
surrogates based on radial-basis-function networks. In addition, a federated
surrogate management strategy is suggested by designing an acquisition function
that takes into account the information of both the global and local surrogate
models. Empirical studies on a set of widely used benchmark functions in the
presence of various data distributions demonstrate the effectiveness of the
proposed framework.
