# A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning

arXiv: https://arxiv.org/abs/2011.10464 · HTML: https://mlsanity.com/paper/2011.10464/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

## Built on by (most cited first, 30 of 41)

- [FedCorr: Multi-Stage Federated Learning for Label Noise Correction](https://mlsanity.com/paper/2204.04677/) (2022)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [Towards Verifiable Federated Learning](https://mlsanity.com/paper/2202.08310/) (2022)
- [FedGS: Federated Graph-based Sampling with Arbitrary Client Availability](https://mlsanity.com/paper/2211.13975/) (2022)
- [Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources](https://mlsanity.com/paper/2305.06217/) (2023)
- [Secure Federated Learning against Model Poisoning Attacks via Client Filtering](https://mlsanity.com/paper/2304.00160/) (2023)
- [Towards Fair Graph Federated Learning via Incentive Mechanisms](https://mlsanity.com/paper/2312.13306/) (2023)
- [Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks](https://mlsanity.com/paper/2502.04850/) (2025)
- [Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement](https://mlsanity.com/paper/2201.02410/) (2022)
- [Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences](https://mlsanity.com/paper/2311.14127/) (2023)
- [On the Effect of Defections in Federated Learning and How to Prevent Them](https://mlsanity.com/paper/2311.16459/) (2023)
- [ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning](https://mlsanity.com/paper/2405.20975/) (2024)
- [Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms](https://mlsanity.com/paper/2207.02337/) (2022)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning](https://mlsanity.com/paper/2405.18291/) (2024)
- [CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning](https://mlsanity.com/paper/2501.12344/) (2025)
- [Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency](https://mlsanity.com/paper/2206.05406/) (2022)
- [Fairness and Privacy in Federated Learning and Their Implications in Healthcare](https://mlsanity.com/paper/2308.07805/) (2023)
- [FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?](https://mlsanity.com/paper/2405.13879/) (2024)
- [Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives](https://mlsanity.com/paper/2406.10884/) (2024)
- [Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2502.05547/) (2025)
- [Bant: Byzantine Antidote via Trial Function and Trust Scores](https://mlsanity.com/paper/2505.07614/) (2025)
- [Fairness in Federated Learning: Trends, Challenges, and Opportunities](https://mlsanity.com/paper/2509.00799/) (2025)
- [FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis](https://mlsanity.com/paper/2503.10567/) (2025)
- [A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction](https://mlsanity.com/paper/2201.02783/) (2022)
- [Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning](https://mlsanity.com/paper/2310.02651/) (2023)
- [Privacy-preserving gradient-based fair federated learning](https://mlsanity.com/paper/2407.13881/) (2024)
- [TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic](https://mlsanity.com/paper/2410.12316/) (2024)
- [Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks](https://mlsanity.com/paper/2510.01261/) (2025)
- [Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions](https://mlsanity.com/paper/2603.23472/) (2026)

## Abstract (arXiv)

Federated learning (FL) is an emerging practical framework for effective and scalable machine learning among multiple participants, such as end users, organizations and companies. However, most existing FL or distributed learning frameworks have not well addressed two important issues together: collaborative fairness and adversarial robustness (e.g. free-riders and malicious participants). In conventional FL, all participants receive the global model (equal rewards), which might be unfair to the high-contributing participants. Furthermore, due to the lack of a safeguard mechanism, free-riders or malicious adversaries could game the system to access the global model for free or to sabotage it. In this paper, we propose a novel Robust and Fair Federated Learning (RFFL) framework to achieve collaborative fairness and adversarial robustness simultaneously via a reputation mechanism. RFFL maintains a reputation for each participant by examining their contributions via their uploaded gradients (using vector similarity) and thus identifies non-contributing or malicious participants to be removed. Our approach differentiates itself by not requiring any auxiliary/validation dataset. Extensive experiments on benchmark datasets show that RFFL can achieve high fairness and is very robust to different types of adversaries while achieving competitive predictive accuracy.