Distributed Event-Triggered Algorithms for Finite-Time Privacy-Preserving Quantized Average Consensus

In this article, we consider the problem of privacy preservation in average consensus when communication among nodes is quantized. More specifically, a setting where network can be curious, while certain want to ensure that their initial states cannot inferred exactly by these curious nodes. Curious are not malicious, i.e., they try identify other based on data receive during operation (and some them might even collude) but do interfere computation any way. Each node (including nodes) opt execute privacy-preserving algorithm (so as reveal state it contributes calculation) or its underlying (plain) (if concern). We propose two event-triggered quantized algorithms. Under topological conditions, both proposed algorithms allow adopt protocols preserve and at same time obtain, after finite number steps, exact processing transmitting quantized information. also present illustrative examples comparisons our against existing literature discuss motivating application which smart meters grid collect real-time demands manner.