# Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction

arXiv: https://arxiv.org/abs/2404.14389 · HTML: https://mlsanity.com/paper/2404.14389/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Mitigating Sybils in Federated Learning Poisoning](https://mlsanity.com/paper/1808.04866/) (2018)
- [Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation](https://mlsanity.com/paper/1903.03936/) (2019)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients](https://mlsanity.com/paper/2203.08669/) (2022)
- [AFLGuard: Byzantine-robust Asynchronous Federated Learning](https://mlsanity.com/paper/2212.06325/) (2022)
- [Dual Attention-Based Federated Learning for Wireless Traffic Prediction](https://mlsanity.com/paper/2110.05183/) (2021)
- [Poisoning Federated Recommender Systems with Fake Users](https://mlsanity.com/paper/2402.11637/) (2024)
- [Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks](https://mlsanity.com/paper/2403.03149/) (2024)
- [A Review of Network Traffic Analysis and Prediction Techniques](https://mlsanity.com/paper/1507.05722/) (2015)

## Built on by (most cited first, 10 of 10)

- [Byzantine-Robust Decentralized Federated Learning](https://mlsanity.com/paper/2406.10416/) (2024)
- [Traceback of Poisoning Attacks to Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.21668/) (2025)
- [Do We Really Need to Design New Byzantine-robust Aggregation Rules?](https://mlsanity.com/paper/2501.17381/) (2025)
- [Provably Robust Federated Reinforcement Learning](https://mlsanity.com/paper/2502.08123/) (2025)
- [Poisoning Attacks and Defenses to Federated Unlearning](https://mlsanity.com/paper/2501.17396/) (2025)
- [Toward Malicious Clients Detection in Federated Learning](https://mlsanity.com/paper/2505.09110/) (2025)
- [Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning](https://mlsanity.com/paper/2507.00423/) (2025)
- [When the Server Steps In: Calibrated Updates for Fair Federated Learning](https://mlsanity.com/paper/2601.05352/) (2026)
- [SecureAFL: Secure Asynchronous Federated Learning](https://mlsanity.com/paper/2604.03862/) (2026)
- [Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning](https://mlsanity.com/paper/2607.08137/) (2026)

## Abstract (arXiv)

Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data. This makes it ideal for applications like wireless traffic prediction (WTP), which plays a crucial role in optimizing network resources, enabling proactive traffic flow management, and enhancing the reliability of downstream communication-aided applications, such as IoT devices, autonomous vehicles, and industrial automation systems. Despite its promise, the security aspects of FL-based distributed wireless systems, particularly in regression-based WTP problems, remain inadequately investigated. In this paper, we introduce a novel fake traffic injection (FTI) attack, designed to undermine the FL-based WTP system by injecting fabricated traffic distributions with minimal knowledge. We further propose a defense mechanism, termed global-local inconsistency detection (GLID), which strategically removes abnormal model parameters that deviate beyond a specific percentile range estimated through statistical methods in each dimension. Extensive experimental evaluations, performed on real-world wireless traffic datasets, demonstrate that both our attack and defense strategies significantly outperform existing baselines.