# Robust Reputation-Driven Crowdsourced Federated Learning

arXiv: https://arxiv.org/abs/2608.08574 · HTML: https://mlsanity.com/paper/2608.08574/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection](https://mlsanity.com/paper/2201.00763/) (2022)
- [Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity](https://mlsanity.com/paper/2302.01772/) (2023)
- [Multi-metrics adaptively identifies backdoors in Federated learning](https://mlsanity.com/paper/2303.06601/) (2023)
- [LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models](https://mlsanity.com/paper/2404.13236/) (2024)
- [VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning](https://mlsanity.com/paper/2501.04319/) (2025)
- [AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated Learning](https://mlsanity.com/paper/2501.04331/) (2025)
- [SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)](https://mlsanity.com/paper/2602.23167/) (2026)
- [DARTIC: Decentralized Anonymous Reputation at Scale for Trustworthy Crowdsourcing](https://mlsanity.com/paper/2605.18146/) (2026)

## Abstract (arXiv)

Crowdsourced Federated Learning (CrowdFL) extends traditional federated learning by enabling open and heterogeneous participation through a crowdsourcing paradigm. In this setting, reputation-driven incentive mechanisms are commonly employed to guide worker selection and enhance trustworthiness. While such approaches improve participant reliability, existing frameworks largely overlook the quantification of their robustness against stealthy adversaries, particularly those capable of evading standard detection mechanisms. To fill this gap, this paper proposes R2CFL, a robust reputation-driven CrowdFL framework. R2CFL introduces a robust reputation model coupled with a nearest neighbor mixing (R2-NNM) defense mechanism that links reputation evolution with the filtering of updates during aggregation. The proposed mechanism prevents stealthy attackers from gradually accumulating trust and influencing future tasks. Experimental results demonstrate that R2-NNM matches or surpasses state-of-the-art Byzantine-robust and backdoor defense mechanisms against adaptive attackers. Furthermore, when integrated with existing detect-and-filter defenses, the proposed reputation model faithfully captures the statistical robustness of the underlying defense by producing reputation scores that closely reflect its true positive and false positive characteristics.