The federated filter is a near globally optimal distributed estimation method based on rigorous information-sharing principles. It is applied here to multi-perform target tracking systems where platform-level target tracks are fused across platforms into global tracks. Global track accuracy is enhanced by the geometric diversity of measurements from different platforms, in addition to the greater number of measurements. On each platform, the federated filter employs dual platform-level filters (PFs) for each track. The primary PFs are locally optimal, and contain all the information gathered from the platform track sensors. The secondary PFs are identical except that they contain only the incremental track information gained since the last fusion cycle. On each platform, global track solutions are near globally optimal because they receive only new tracklet information from the onboard and off-board PFs, and don to re-use old platform-level information. Logistically, platforms can operate autonomously with no need for synchronized operations or master/slave designations; the architecture is completely symmetric. Platforms can enter or leave the group with no changes in other global trackers. Communications bandwidth is minimal because global tracks need not be shared. The paper describes the theoretical basis of the federated fusing filter, the related data association functions, and preliminary simulation results.