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As peer-to-peer (P2P) systems receive growing acceptance, the need of identifying 'frequent items' in such systems appears in a variety of applications. In this paper, we define the problem of identifying frequent items (IFI) and propose an efficient in-network processing technique, called in-network filtering (netFilter), to address this important fundamental problem. netFilter operates in two phases: 1) candidate filtering: data items are grouped into item groups to obtain aggregates for pruning of infrequent items; and 2) candidate verification: the aggregates for the remaining candidate items are obtained to filter out false frequent items. We address various issues faced in realizing netFilter, including aggregate computation, candidate set optimization, and candidate set materialization. In addition, we analyze the performance of netFilter, derive the optimal setting analytically, and discuss how to achieve the optimal setting in practice. Finally, we validate the effectiveness of netFilter through extensive simulation.