# Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding

arXiv: https://arxiv.org/abs/2409.00848 · HTML: https://mlsanity.com/paper/2409.00848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Optimal Learning of Mallows Block Model](https://mlsanity.com/paper/1906.01009/) (2019)
- [Concentric mixtures of Mallows models for top-k rankings: sampling and identifiability](https://mlsanity.com/paper/2010.14260/) (2020)
- [FRL: Federated Rank Learning](https://mlsanity.com/paper/2110.04350/) (2021)
- [Fair Rank Aggregation](https://mlsanity.com/paper/2308.10499/) (2023)
- [Private Rank Aggregation in Central and Local Models](https://mlsanity.com/paper/2112.14652/) (2021)
- [On A Mallows-type Model For (Ranked) Choices](https://mlsanity.com/paper/2207.01783/) (2022)
- [Efficient Rank Aggregation via Lehmer Codes](https://mlsanity.com/paper/1701.09083/) (2017)

## Built on by (most cited first, 1 of 1)

- [Decentralized Ranking Aggregation via Gossip: Convergence and Robustness](https://mlsanity.com/paper/2602.22847/) (2026)

## Abstract (arXiv)

Rank aggregation combines multiple ranked lists into a consensus ranking. In fields like biomedical data sharing, rankings may be distributed and require privacy. This motivates the need for federated rank aggregation protocols, which support distributed, private, and communication-efficient learning across multiple clients with local data. We present the first known federated rank aggregation methods using Borda scoring and Lehmer codes, focusing on the sample complexity for federated algorithms on Mallows distributions with a known scaling factor φ and an unknown centroid permutation σ0. Federated Borda approach involves local client scoring, nontrivial quantization, and privacy-preserving protocols. We show that for φ ∈ [0,1), and arbitrary σ0 of length N, it suffices for each of the L clients to locally aggregate maxC1(φ), C2(φ)frac1Llog fracNδ rankings, where C1(φ) and C2(φ) are constants, quantize the result, and send it to the server who can then recover σ0 with probability ≥ 1-δ. Communication complexity scales as NL log N. Our results represent the first rigorous analysis of Borda's method in centralized and distributed settings under the Mallows model. Federated Lehmer coding approach creates a local Lehmer code for each client, using a coordinate-majority aggregation approach with specialized quantization methods for efficiency and privacy. We show that for φ+φ2<1+φN, and arbitrary σ0 of length N, it suffices for each of the L clients to locally aggregate maxC3(φ), C4(φ)frac1Llog fracNδ rankings, where C3(φ) and C4(φ) are constants. Clients send truncated Lehmer coordinate histograms to the server, which can recover σ0 with probability ≥ 1-δ. Communication complexity is ~ O(Nlog NLlog L).