Federated Learning for intrusion detection system: Concepts, challenges and future directions
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A deep reinforcement learning based algorithm is presented where DRL is responsible for allocation awards at the aggregator and selecting devices to suggest the learning model for their data.
“To tackle that drawback, a deep reinforcement learning (DRL) based algorithm is presented in [68], where DRL is responsible for allocation awards at the aggregator and selecting devices to suggest the learning model for their data.”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [68]
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Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
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- Authors
- Agrawal, S., Sarkar, S., Aouedi, O., Yenduri, G., Piamrat, K., Alazab, M., Bhattacharya, S., Maddikunta, P. K. R., & Gadekallu, T. R.
- Affiliations
- Vellore Institute of Technology University (IN)
- Laboratoire des Sciences du Numérique de Nantes (FR)
- Charles Darwin University (AU)
- Corresponding author
- Gadekallu, Thippa Reddy
- ORCID
- Venue
- Computer Communications
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 195
- Pages
- 346-361
- ISSN
- 0140-3664 (ISSN-L)
- OpenAlex
- W3171802458
- Cited by
- 457
- References
- 120
- Citations by year
- 2026: 80 · 2025: 165 · 2024: 114 · 2023: 67 · 2022: 28 · 2021: 3
- FWCI
- 41.50
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Computer Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- HAL (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- federated learning, intrusion detection systems, privacy-preserving machine learning, anomaly detection, edge computing, decentralized learning, network security, distributed networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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