Abstract
This paper makes further generalisations based on the application of edge-based state decomposition methods on average consensus. The edge-based state decomposition method is a privacy-preserving technique that leverages the number of neighbours to decompose node information, effectively safeguarding the privacy of nodes in average consensus, even in the absence of reliable neighbours. Building upon this method, our research expands its applicability to bipartite consensus and scale consensus. In bipartite consensus, the method can also accurately achieve consensus while guaranteeing privacy. In contrast, scale consensus effectively protects the privacy of the final consistency result. In addition, this paper refines the privacy proofs for different enemies. Finally, the simulation results presented at the end of the paper validate the effectiveness of the proposed approach.
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