We study distributed games played on non-deterministic asynchronous automata which feature a central decision maker process that participates in all key decision making tasks. In these partial-information games, processes use their causal past to respond to scheduling choices made by the scheduler and cooperatively strategize as a team to achieve the winning objective. We show that the problem of deciding the existence of a distributed winning strategy is efficiently solvable for global safety and local parity objectives. We provide algorithmic solutions that match their computational hardness. We formulate the notion of a finite-state distributed strategy which allows to quantify its distributed memory requirements. For the aforementioned objectives, we establish that finite-state distributed winning strategies always exist. In fact, we provide novel constructions of such winning strategies which are shown to have almost optimal amount of distributed memory. We also show that a natural extension of the model with two decision making processes is undecidable.