1. A method for distributed computing, comprising: at a plurality of computing nodes, processing respective sets of data and calculating respective local values of one or more statistical parameters characterizing the sets of the data; defining a global condition, which is violated when a function defined over a weighted average of the respective local values crosses a predetermined threshold; separating the global condition into a plurality of local constraints, comprising a respective local constraint to be evaluated by each of the nodes based on the respective local values, such that violation of the respective local constraint in at least one of the nodes indicates a violation of the global condition; evaluating the respective local constraint independently at each of the nodes; and when at least one of the nodes detects that the respective local constraint is violated, outputting an indication that the global condition has been violated, said evaluating the local constraint comprising, at each node: representing the local values as a local statistics vector in a d-dimensional vector space spanned by the statistical parameters; adjusting the local statistics vector by an offset vector to produce a drift vector, such that a weighted sum of the offset vectors used by the respective nodes equals zero; defining a d-dimensional geometrical shape in the vector space, such that a union of the d-dimensional geometrical shapes defined by the nodes bounds a convex hull of the drift vectors; and determining that the local condition is violated by detecting that the geometrical shape encloses at least two vectors such that the function evaluated at the two vectors produces two respective values that fall on opposite sides of the threshold.