# Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy

arXiv: https://arxiv.org/abs/2101.09878 · HTML: https://mlsanity.com/paper/2101.09878/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 38 references that are in the map.
- First posted on arXiv 25 Jan 2021.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences](https://mlsanity.com/paper/1807.01647/) (2018)
- [Subsampled R'enyi Differential Privacy and Analytical Moments Accountant](https://mlsanity.com/paper/1808.00087/) (2018)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [Heterogeneous Differential Privacy](https://mlsanity.com/paper/1504.06998/) (2015)
- [One-sided Differential Privacy](https://mlsanity.com/paper/1712.05888/) (2017)

## Built on by (most cited first, 14 of 14)

- [Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey](https://mlsanity.com/paper/2412.13840/) (2024)
- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments](https://mlsanity.com/paper/2501.15563/) (2025)
- [Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework](https://mlsanity.com/paper/2508.03989/) (2025)
- [Exploring the Intersection between Neural Architecture Search and Continual Learning](https://mlsanity.com/paper/2206.05625/) (2022)
- [Contrastive Continual Learning for Model Adaptability in Internet of Things](https://mlsanity.com/paper/2602.04881/) (2026)
- [Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy](https://mlsanity.com/paper/2505.13655/) (2025)
- [Backdoor Attacks on Multi-modal Contrastive Learning](https://mlsanity.com/paper/2601.11006/) (2026)
- [Contrastive Learning for Privacy Enhancements in Industrial Internet of Things](https://mlsanity.com/paper/2602.00515/) (2026)
- [Backdoor Attacks on Contrastive Continual Learning for IoT Systems](https://mlsanity.com/paper/2602.13062/) (2026)
- [CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement](https://mlsanity.com/paper/2603.11526/) (2026)
- [Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems](https://mlsanity.com/paper/2605.27674/) (2026)
- [Continual Backdoor Training in IoT/CPS](https://mlsanity.com/paper/2606.14987/) (2026)
- [CLIP-guided Diffusion Model for Backdoor Generation in Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2606.22837/) (2026)

## Abstract (arXiv)

Internet of Things (IoT) devices are becoming increasingly popular and are influencing many application domains such as healthcare and transportation. These devices are used for real-world applications such as sensor monitoring, real-time control. In this work, we look at differentially private (DP) neural network (NN) based network intrusion detection systems (NIDS) to detect intrusion attacks on networks of such IoT devices. Existing NN training solutions in this domain either ignore privacy considerations or assume that the privacy requirements are homogeneous across all users. We show that the performance of existing differentially private stochastic methods degrade for clients with non-identical data distributions when clients' privacy requirements are heterogeneous. We define a cohort-based (ε,δ)-DP framework that models the more practical setting of IoT device cohorts with non-identical clients and heterogeneous privacy requirements. We propose two novel continual-learning based DP training methods that are designed to improve model performance in the aforementioned setting. To the best of our knowledge, ours is the first system that employs a continual learning-based approach to handle heterogeneity in client privacy requirements. We evaluate our approach on real datasets and show that our techniques outperform the baselines. We also show that our methods are robust to hyperparameter changes. Lastly, we show that one of our proposed methods can easily adapt to post-hoc relaxations of client privacy requirements.