DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs
Modern distributed systems generate massive volumes of log data that are critical for detecting anomalies and cyber threats. However, in real world settings, these logs are often distributed across multiple organizations and cannot be centralized due to privacy and security constraints. Existing log anomaly detection methods, including recent large language model (LLM) based approaches, largely rely on centralized training and are not suitable for such environments. In this paper, we propose DP-FLogTinyLLM, a privacy preserving federated framework for log anomaly detection using parameter efficient LLMs. Our approach enables collaborative learning without sharing raw log data by integrati...
| Authors | Isaiah Thompson, Tanmay Sen, Ritwik Bhattacharya |
|---|---|
| Primary category | cs.CR |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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