The present paper studies local distributed graph problems in highly dynamic networks. Communication and changes of the graph happen in synchronous rounds and our algorithms always, i.e., in every round, satisfy non-trivial guarantees, no matter how dynamic the network is. We define a (in our view) natural generalization of static graph problems to the dynamic graph setting. Throughout the execution of an algorithm we consider a sliding window over the last $T$, e.g., polylogarithmic, rounds. Then, in some round, the feasibility of an output only depends on the topology of the graphs in the current sliding window and we call a feasible output a $T$-dynamic solution. The guarantees of a $T$-dynamic solution become stronger the more stable the graph is during this sliding window and, in particular, they coincide with the definition of the static graph problem if the graph is static throughout the window. We further present an abstract framework that allows to develop algorithms that output $T$-dynamic solutions in all rounds. The resulting algorithms have another desirable property: If a constant neighborhood around some part of the graph is stable during an interval $[t_1,t_2]$, the algorithms compute a static solution for this part of the graph throughout the interval $[t_1+T',t_2]$ for some (small) $T'>0$. We demonstrate our generic framework with two sample problems that abstract basic operations in dynamic networks, namely $\textit{(degree+1)-vertex coloring}$ and $\textit{maximal independent set (MIS)}$. To illustrate the given guarantees consider the vertex coloring problem: The sliding window of our (randomized) algorithm is of length $T=O(\log n)$ and any conflict between two nodes caused by a newly inserted edge is resolved within that time. During this conflict resolving both nodes always output colors that are not in conflict with their respective 'old' neighbors.