A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization
This addresses optimization challenges in scenarios with privacy constraints and distributed data, though it is incremental as it adapts existing federated learning to evolutionary algorithms.
The paper tackles the problem of expensive multi/many-objective optimization when data is distributed and private by proposing a federated data-driven evolutionary algorithm, achieving competitive performance compared to state-of-the-art methods on benchmark problems.
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.