# Asynchronous Push/Pull Gossip for Data-Set aggregation

All algorithms regarding asynchronous push/pull gossip (APPG) I can find are either on spreading a single piece of information, or on data aggregation like average sums.

I'm looking for an APPG algorithm in the following scenario:

Suppose we have a network of machines $$G(t)$$, and a set $$S(t)$$ of information, such that each member $$g\in G(t)$$ holds a subset of $$S_i(t) \subset S(t)$$. These sets might change over time. The goal is to use APPG, to approximate $$S(t)$$ by $$S_i(t)$$ for all members.

$$S(t)$$ can grow over time (but not shrink), by members extending their subsets $$S_i(t)$$, so the algorithm might continue forever, with the goal to get each $$S_i(t)$$ always as close to $$S(t)$$ as possible, while $$S(t)$$ grows.

The problem here is, that I don't really know how to search for the appropriate research papers. Its not data aggregation as far as I can see, nor is it really state or data-set replication. Its data set aggregation, IMO, but I can not find anything useful.

Maybe someone can also expand the tags a bit.