In this paper, we present an unified framework that encompasses both particle swarm optimization (PSO) and federated learning (FL). This unified framework shows that we can understand both PSO and FL in terms of a function to be optimized by a set of agents but in which agents have different privacy requirements. PSO is the most relaxed case, and FL considers slightly stronger constraints. Even stronger privacy requirements can be considered which will lead to still stronger privacy-preserving solutions. Differentially private solutions as well as local differential privacy/reidentification privacy for agents opinions are the additional privacy models to be considered. In this paper, we discuss this framework and the different privacy-related alternatives. We present experiments that show how the additional privacy requirements degrade the results of the system. To that end, we consider optimization problems compatible with both PSO and FL.