# Decentralized Ranking Aggregation via Gossip: Convergence and Robustness

arXiv: https://arxiv.org/abs/2602.22847 · HTML: https://mlsanity.com/paper/2602.22847/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF](https://mlsanity.com/paper/2312.08358/) (2023)
- [Byzantine-Robust Decentralized Learning via ClippedGossip](https://mlsanity.com/paper/2202.01545/) (2022)
- [Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial Iterations](https://mlsanity.com/paper/1805.08531/) (2018)
- [Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks](https://mlsanity.com/paper/2305.10284/) (2023)
- [Unified Breakdown Analysis for Byzantine Robust Gossip](https://mlsanity.com/paper/2410.10418/) (2024)
- [Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications](https://mlsanity.com/paper/2201.08105/) (2022)
- [Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means](https://mlsanity.com/paper/2505.17836/) (2025)
- [Optimizing positional scoring rules for rank aggregation](https://mlsanity.com/paper/1609.07460/) (2016)
- [Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues](https://mlsanity.com/paper/2303.12878/) (2023)
- [Byzantine Multi-Agent Optimization: Part II](https://mlsanity.com/paper/1507.01845/) (2015)
- [Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding](https://mlsanity.com/paper/2409.00848/) (2024)

## Abstract (arXiv)

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, textiti.e., when all the ranking data to be aggregated can be brought together in a single computing unit. For many technologies (textite.g. peer-to-peer networks, IoT, multi-agent systems), extending the ability to calculate consensus rankings with guarantees of convergence and resilience to potential contamination in a decentralized setting, 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 and resilient consensus on collective rankings in a decentralized setting, thereby raising new questions, robustness to corrupted nodes, and scalability through reduced communication costs in particular. The approach proposed and analyzed here relies on the robustness guarantees offered by random gossip communication, which allows autonomous agents to compute a global ranking consensus using local interactions only, without coordination or a central authority.