# Free-rider Attacks on Model Aggregation in Federated Learning

arXiv: https://arxiv.org/abs/2006.11901 · HTML: https://mlsanity.com/paper/2006.11901/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 30 of 57)

- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents](https://mlsanity.com/paper/2505.02077/) (2025)
- [MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients](https://mlsanity.com/paper/2203.08669/) (2022)
- [Mechanisms that Incentivize Data Sharing in Federated Learning](https://mlsanity.com/paper/2207.04557/) (2022)
- [FedIPR: Ownership Verification for Federated Deep Neural Network Models](https://mlsanity.com/paper/2109.13236/) (2021)
- [An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning](https://mlsanity.com/paper/2302.07173/) (2023)
- [LoMar: A Local Defense Against Poisoning Attack on Federated Learning](https://mlsanity.com/paper/2201.02873/) (2022)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Preserved central model for faster bidirectional compression in distributed settings](https://mlsanity.com/paper/2102.12528/) (2021)
- [Privacy and Efficiency of Communications in Federated Split Learning](https://mlsanity.com/paper/2301.01824/) (2023)
- [FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs](https://mlsanity.com/paper/2306.04959/) (2023)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective](https://mlsanity.com/paper/2311.16065/) (2023)
- [Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning](https://mlsanity.com/paper/2306.05592/) (2023)
- [Federated Unlearning: a Perspective of Stability and Fairness](https://mlsanity.com/paper/2402.01276/) (2024)
- [Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing](https://mlsanity.com/paper/2407.20830/) (2024)
- [MARS: A Malignity-Aware Backdoor Defense in Federated Learning](https://mlsanity.com/paper/2509.20383/) (2025)
- [Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks](https://mlsanity.com/paper/2212.11194/) (2022)
- [Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution](https://mlsanity.com/paper/2407.15881/) (2024)
- [Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency](https://mlsanity.com/paper/2206.05406/) (2022)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [Contribution Evaluation in Federated Learning: Examining Current Approaches](https://mlsanity.com/paper/2311.09856/) (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)
- [Tackling Selfish Clients in Federated Learning](https://mlsanity.com/paper/2407.15402/) (2024)
- [FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning](https://mlsanity.com/paper/2501.19091/) (2025)
- [Fake or Compromised? Making Sense of Malicious Clients in Federated Learning](https://mlsanity.com/paper/2403.06319/) (2024)

## Abstract (arXiv)

Free-rider attacks against federated learning consist in dissimulating participation to the federated learning process with the goal of obtaining the final aggregated model without actually contributing with any data. This kind of attacks is critical in sensitive applications of federated learning, where data is scarce and the model has high commercial value. We introduce here the first theoretical and experimental analysis of free-rider attacks on federated learning schemes based on iterative parameters aggregation, such as FedAvg or FedProx, and provide formal guarantees for these attacks to converge to the aggregated models of the fair participants. We first show that a straightforward implementation of this attack can be simply achieved by not updating the local parameters during the iterative federated optimization. As this attack can be detected by adopting simple countermeasures at the server level, we subsequently study more complex disguising schemes based on stochastic updates of the free-rider parameters. We demonstrate the proposed strategies on a number of experimental scenarios, in both iid and non-iid settings. We conclude by providing recommendations to avoid free-rider attacks in real world applications of federated learning, especially in sensitive domains where security of data and models is critical.