  Data-driven optimization has found many successful applications in the real
world and received increased attention in the field of evolutionary
optimization. Most existing algorithms assume that the data used for
optimization is always available on a central server for construction of
surrogates. This assumption, however, may fail to hold when the data must be
collected in a distributed way and is subject to privacy restrictions. This
paper aims to propose a federated data-driven evolutionary
multi-/many-objective optimization algorithm. To this end, we leverage
federated learning for surrogate construction so that multiple clients
collaboratively train a radial-basis-function-network as the global surrogate.
Then a new federated acquisition function is proposed for the central server to
approximate the objective values using the global surrogate and estimate the
uncertainty level of the approximated objective values based on the local
models. The performance of the proposed algorithm is verified on a series of
multi/many-objective benchmark problems by comparing it with two
state-of-the-art surrogate-assisted multi-objective evolutionary algorithms.
