# Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks

arXiv: https://arxiv.org/abs/2211.03489 · HTML: https://mlsanity.com/paper/2211.03489/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Backdoor Learning: A Survey](https://mlsanity.com/paper/2007.08745/) (2020)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020)
- [Defending against Backdoors in Federated Learning with Robust Learning Rate](https://mlsanity.com/paper/2007.03767/) (2020)
- [Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks](https://mlsanity.com/paper/1803.06975/) (2018)
- [Provably Secure Federated Learning against Malicious Clients](https://mlsanity.com/paper/2102.01854/) (2021)
- [Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis](https://mlsanity.com/paper/2203.11633/) (2022)
- [Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning](https://mlsanity.com/paper/2205.11779/) (2022)
- [Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks](https://mlsanity.com/paper/2108.00701/) (2021)

## Built on by (most cited first, 2 of 2)

- [Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients](https://mlsanity.com/paper/2401.09267/) (2024)
- [University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system](https://mlsanity.com/paper/2512.05468/) (2025)

## Abstract (arXiv)

Wireless ad hoc federated learning (WAFL) is a fully decentralized collaborative machine learning framework organized by opportunistically encountered mobile nodes. Compared to conventional federated learning, WAFL performs model training by weakly synchronizing the model parameters with others, and this shows great resilience to a poisoned model injected by an attacker. In this paper, we provide our theoretical analysis of the WAFL's resilience against model poisoning attacks, by formulating the force balance between the poisoned model and the legitimate model. According to our experiments, we confirmed that the nodes directly encountered the attacker has been somehow compromised to the poisoned model but other nodes have shown great resilience. More importantly, after the attacker has left the network, all the nodes have finally found stronger model parameters combined with the poisoned model. Most of the attack-experienced cases achieved higher accuracy than the no-attack-experienced cases.