Large-scale distributed data integration systems have to deal with important query processing costs which are essentially due to the high communication overload between data peers. Caching techniques can drastically reduce processing and communication cost. We propose a new distributed caching strategy that reduces redundant caching decisions of individual peers. We estimate cache redundancy by a distributed algorithm without additional messages. Our simulation experiments show that considering redundancy scores can drastically reduce distributed query execution costs.