StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911884586778624 |
|---|---|
| author | Bouzinis, Pavlos S. Radoglou-Grammatikis, Panagiotis Makris, Ioannis Lagkas, Thomas Argyriou, Vasileios Papadopoulos, Georgios Th. Sarigiannidis, Panagiotis Karagiannidis, George K. |
| author_facet | Bouzinis, Pavlos S. Radoglou-Grammatikis, Panagiotis Makris, Ioannis Lagkas, Thomas Argyriou, Vasileios Papadopoulos, Georgios Th. Sarigiannidis, Panagiotis Karagiannidis, George K. |
| contents | Federated learning (FL) is a decentralized learning technique that enables participating devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model without revealing their raw data to a third party. Due to its privacy-preserving nature, FL has sparked widespread attention for building Intrusion Detection Systems (IDS) within the realm of cybersecurity. However, the data heterogeneity across participating domains and entities presents significant challenges for the reliable implementation of an FL-based IDS. In this paper, we propose an effective method called Statistical Averaging (StatAvg) to alleviate non-independently and identically (non-iid) distributed features across local clients' data in FL. In particular, StatAvg allows the FL clients to share their individual data statistics with the server, which then aggregates this information to produce global statistics. The latter are shared with the clients and used for universal data normalisation. It is worth mentioning that StatAvg can seamlessly integrate with any FL aggregation strategy, as it occurs before the actual FL training process. The proposed method is evaluated against baseline approaches using datasets for network and host Artificial Intelligence (AI)-powered IDS. The experimental results demonstrate the efficiency of StatAvg in mitigating non-iid feature distributions across the FL clients compared to the baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13062 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems Bouzinis, Pavlos S. Radoglou-Grammatikis, Panagiotis Makris, Ioannis Lagkas, Thomas Argyriou, Vasileios Papadopoulos, Georgios Th. Sarigiannidis, Panagiotis Karagiannidis, George K. Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning Federated learning (FL) is a decentralized learning technique that enables participating devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model without revealing their raw data to a third party. Due to its privacy-preserving nature, FL has sparked widespread attention for building Intrusion Detection Systems (IDS) within the realm of cybersecurity. However, the data heterogeneity across participating domains and entities presents significant challenges for the reliable implementation of an FL-based IDS. In this paper, we propose an effective method called Statistical Averaging (StatAvg) to alleviate non-independently and identically (non-iid) distributed features across local clients' data in FL. In particular, StatAvg allows the FL clients to share their individual data statistics with the server, which then aggregates this information to produce global statistics. The latter are shared with the clients and used for universal data normalisation. It is worth mentioning that StatAvg can seamlessly integrate with any FL aggregation strategy, as it occurs before the actual FL training process. The proposed method is evaluated against baseline approaches using datasets for network and host Artificial Intelligence (AI)-powered IDS. The experimental results demonstrate the efficiency of StatAvg in mitigating non-iid feature distributions across the FL clients compared to the baseline methods. |
| title | StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems |
| topic | Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2405.13062 |