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[2602.22847] Decentralized Ranking Aggregation: Gossip Algorithms for Borda and Copeland Consensus
Summary
This article explores decentralized ranking aggregation using gossip algorithms for Borda and Copeland consensus, addressing challenges in preference analysis across distributed networks.
Why It Matters
As decentralized systems become increasingly prevalent, effective ranking aggregation methods are crucial for applications like peer-to-peer networks and IoT. This research provides new insights into achieving reliable consensus without centralized authority, enhancing scalability and robustness against node corruption.
Key Takeaways
- Introduces gossip algorithms for decentralized ranking aggregation.
- Analyzes Borda and Copeland consensus methods in distributed settings.
- Demonstrates convergence guarantees and empirical evaluations.
- Addresses challenges like robustness to corrupted nodes.
- Highlights reduced communication costs for scalable solutions.
Computer Science > Machine Learning arXiv:2602.22847 (cs) [Submitted on 26 Feb 2026] Title:Decentralized Ranking Aggregation: Gossip Algorithms for Borda and Copeland Consensus Authors:Anna Van Elst, Kerrian Le Caillec, Igor Colin, Stephan Clémençon View a PDF of the paper titled Decentralized Ranking Aggregation: Gossip Algorithms for Borda and Copeland Consensus, by Anna Van Elst and 3 other authors View PDF HTML (experimental) Abstract:The concept of ranking aggregation plays a central role in preference analysis, and numerous algorithms for calculating median rankings, often originating in social choice theory, have been documented in the literature, offering theoretical guarantees in a centralized setting, i.e., when all the ranking data to be aggregated can be brought together in a single computing unit. For many technologies (e.g. peer-to-peer networks, IoT, multi-agent systems), extending the ability to calculate consensus rankings with guarantees in a decentralized setting, i.e., when preference data is initially distributed across a communicating network, remains a major methodological challenge. Indeed, in recent years, the literature on decentralized computation has mainly focused on computing or optimizing statistics such as arithmetic means using gossip algorithms. The purpose of this article is precisely to study how to achieve reliable consensus on collective rankings using classical rules (e.g. Borda, Copeland) in a decentralized setting, thereby raising n...