# OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT

arXiv: https://arxiv.org/abs/2510.05180 · HTML: https://mlsanity.com/paper/2510.05180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Importance Estimation for Neural Network Pruning](https://mlsanity.com/paper/1906.10771/) (2019)
- [DepGraph: Towards Any Structural Pruning](https://mlsanity.com/paper/2301.12900/) (2023)
- [FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT](https://mlsanity.com/paper/2012.06150/) (2020)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [Complexity-Driven CNN Compression for Resource-constrained Edge AI](https://mlsanity.com/paper/2208.12816/) (2022)
- [LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing](https://mlsanity.com/paper/2011.14194/) (2020)
- [A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection](https://mlsanity.com/paper/2111.02791/) (2021)
- [NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNN](https://mlsanity.com/paper/2506.17870/) (2025)
- [EUREKHA: Enhancing User Representation for Key Hackers Identification in Underground Forums](https://mlsanity.com/paper/2411.05479/) (2024)
- [EventHunter: Dynamic Clustering and Ranking of Security Events from Hacker Forum Discussions](https://mlsanity.com/paper/2507.09762/) (2025)

## Abstract (arXiv)

In critical IoT environments, such as smart homes and industrial systems, effective Intrusion Detection Systems (IDS) are essential for ensuring security. However, developing robust IDS solutions remains a significant challenge. Traditional machine learning-based IDS models typically require large datasets, but data sharing is often limited due to privacy and security concerns. Federated Learning (FL) presents a promising alternative by enabling collaborative model training without sharing raw data. Despite its advantages, FL still faces key challenges, such as data heterogeneity (non-IID data) and high energy and computation costs, particularly for resource constrained IoT devices. To address these issues, this paper proposes OptiFLIDS, a novel approach that applies pruning techniques during local training to reduce model complexity and energy consumption. It also incorporates a customized aggregation method to better handle pruned models that differ due to non-IID data distributions. Experiments conducted on three recent IoT IDS datasets, TON_IoT, X-IIoTID, and IDSIoT2024, demonstrate that OptiFLIDS maintains strong detection performance while improving energy efficiency, making it well-suited for deployment in real-world IoT environments.