Distributed Inference With M-Ary Quantized Data in the Presence of Byzantine Attacks
The problem of distributed inference with M-ary quantized data at the sensors is investigated in the presence of Byzantine attacks. The authors assume that the attacker does not have knowledge about either the true state of the phenomenon of interest, or the quantization thresholds used at the sensors. Therefore, the Byzantine nodes attack the inference network by modifying the quantized data to one of the M symbols in the quantization alphabet-set and transmitting the false symbol to the Fusion Center (FC). In this paper, they find the optimal Byzantine attack that blinds any distributed inference network. As the quantization alphabet size increases, a tremendous improvement in the security performance of the distributed inference network is observed.