# DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs

arXiv: https://arxiv.org/abs/2604.19118 · HTML: https://mlsanity.com/paper/2604.19118/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [TinyLlama: An Open-Source Small Language Model](https://mlsanity.com/paper/2401.02385/) (2024)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [LogBERT: Log Anomaly Detection via BERT](https://mlsanity.com/paper/2103.04475/) (2021)
- [LogGPT: Log Anomaly Detection via GPT](https://mlsanity.com/paper/2309.14482/) (2023)
- [LogLLM: Log-based Anomaly Detection Using Large Language Models](https://mlsanity.com/paper/2411.08561/) (2024)
- [DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models](https://mlsanity.com/paper/2509.09097/) (2025)
- [Anomaly Detection via Federated Learning](https://mlsanity.com/paper/2210.06614/) (2022)
- [EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters](https://mlsanity.com/paper/2406.09152/) (2024)
- [LogTinyLLM: Tiny Large Language Models Based Contextual Log Anomaly Detection](https://mlsanity.com/paper/2507.11071/) (2025)
- [Uncovering Privacy Vulnerabilities through Analytical Gradient Inversion Attacks](https://mlsanity.com/paper/2509.18871/) (2025)
- [FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection](https://mlsanity.com/paper/2512.08277/) (2025)

## Abstract (arXiv)

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 integrating federated optimization with differential privacy. To ensure scalability in resource constrained environments, we employ low rank adaptation (LoRA) for efficient fine tuning of Tiny LLMs at each client. Empirical results on the Thunderbird and BGL datasets show that the proposed framework matches the performance of centralized LLM based methods, while incurring additional computational overhead due to privacy mechanisms. Compared to existing federated baselines, DP-FLogTinyLLM consistently achieves higher precision and F1-score, with particularly strong gains on the Thunderbird dataset, highlighting its effectiveness in detecting anomalies while minimizing false positives.