# Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments

arXiv: https://arxiv.org/abs/2509.18044 · HTML: https://mlsanity.com/paper/2509.18044/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security.
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Placed in

- LLM safety and security / [Federated unlearning and poisoning](https://mlsanity.com/safety/federated-unlearning-and-poisoning/)

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [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)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Mitigating Sybils in Federated Learning Poisoning](https://mlsanity.com/paper/1808.04866/) (2018)
- [FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients](https://mlsanity.com/paper/2207.09209/) (2022)
- [NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2011.09144/) (2020)
- [Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation](https://mlsanity.com/paper/2409.01435/) (2024)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy](https://mlsanity.com/paper/2309.03437/) (2023)
- [The Disruptions of 5G on Data-driven Technologies and Applications](https://mlsanity.com/paper/1909.08096/) (2019)
- [AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification](https://mlsanity.com/paper/2311.06996/) (2023)
- [Optimizing Fire Safety: Reducing False Alarms Using Advanced Machine Learning Techniques](https://mlsanity.com/paper/2503.09960/) (2025)

## Built on by (most cited first, 1 of 1)

- [FedTransKD-IDS: Robust Federated Transfer Learning with Knowledge Distillation for Intrusion Detection in IoT](https://mlsanity.com/paper/2608.06447/) (2026)

## Abstract (arXiv)

Federated Learning (FL) in 5G and edge network environments face severe security threats from adversarial clients. Malicious participants can perform label flipping, inject backdoor triggers, or launch Sybil attacks to corrupt the global model. This paper introduces Hybrid Reputation Aggregation (HRA), a novel robust aggregation mechanism designed to defend against diverse adversarial behaviors in FL without prior knowledge of the attack type. HRA combines geometric anomaly detection with momentum-based reputation tracking of clients. In each round, it detects outlier model updates via distance-based geometric analysis while continuously updating a trust score for each client based on historical behavior. This hybrid approach enables adaptive filtering of suspicious updates and long-term penalization of unreliable clients, countering attacks ranging from backdoor insertions to random noise Byzantine failures. We evaluate HRA on a large-scale proprietary 5G network dataset (3M+ records) and the widely used NF-CSE-CIC-IDS2018 benchmark under diverse adversarial attack scenarios. Experimental results reveal that HRA achieves robust global model accuracy of up to 98.66% on the 5G dataset and 96.60% on NF-CSE-CIC-IDS2018, outperforming state-of-the-art aggregators such as Krum, Trimmed Mean, and Bulyan by significant margins. Our ablation studies further demonstrate that the full hybrid system achieves 98.66% accuracy, while the anomaly-only and reputation-only variants drop to 84.77% and 78.52%, respectively, validating the synergistic value of our dual-mechanism approach. This demonstrates HRA's enhanced resilience and robustness in 5G/edge federated learning deployments, even under significant adversarial conditions.