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This work addresses fault-tolerant distributed decision fusion in the presence of sensor faults when local sensors sequentially send their local decisions to a fusion center. This work also proposes a collaborative sensor fault detection (CSFD) scheme for eliminating unreliable local decisions when performing distributed decision fusion. In particular, the concept of pseudo-sensor faults is presented. Due to the difficulty in determining pseudo-sensor faults in real time based on the minimization of the error probability, an upper bound is established on the fusion error probability, where distributions of local decisions are not necessarily identical. Given a pre-designed fusion rule under the assumption of identical local decision rules in fault-free environments, this bound can then characterize the fusion error probability when local decisions are no longer identical due to sensor faults. Hence, a criterion is proposed based on this error bound to determine a set of pseudo-faulty nodes at each time. Once the fusion center identifies the pseudo-faulty nodes, all corresponding local decisions are removed from the computation of likelihood ratios adopted to make the final decision. To enhance the efficiency of the proposed search criterion, another, less complex criterion is developed by integrating the Kullback-Leibler distance. Simulation results indicate that this less complex criterion provides even better fault-tolerance capability in some situations when sensor faults significantly deviate from normally operating sensors. Performance evaluation results also indicate that the fault-tolerance capability of the proposed approach employing a CSFD scheme is superior to conventional decision fusion.