# Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning

arXiv: https://arxiv.org/abs/2506.19246 · HTML: https://mlsanity.com/paper/2506.19246/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://mlsanity.com/paper/2107.05908/) (2021)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration](https://mlsanity.com/paper/2309.11103/) (2023)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems](https://mlsanity.com/paper/2505.16248/) (2025)
- [Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning](https://mlsanity.com/paper/2505.00299/) (2025)

## Built on by (most cited first, 4 of 4)

- [High Fidelity Text to Image Generation with Contrastive Alignment and Structural Guidance](https://mlsanity.com/paper/2508.10280/) (2025)
- [Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models](https://mlsanity.com/paper/2508.14427/) (2025)
- [Graph Neural Network and Transformer Integration for Unsupervised System Anomaly Discovery](https://mlsanity.com/paper/2508.09401/) (2025)
- [Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks](https://mlsanity.com/paper/2509.04973/) (2025)

## Abstract (arXiv)

This paper addresses the increasingly prominent problem of anomaly detection in distributed systems. It proposes a detection method based on federated contrastive learning. The goal is to overcome the limitations of traditional centralized approaches in terms of data privacy, node heterogeneity, and anomaly pattern recognition. The proposed method combines the distributed collaborative modeling capabilities of federated learning with the feature discrimination enhancement of contrastive learning. It builds embedding representations on local nodes and constructs positive and negative sample pairs to guide the model in learning a more discriminative feature space. Without exposing raw data, the method optimizes a global model through a federated aggregation strategy. Specifically, the method uses an encoder to represent local behavior data in high-dimensional space. This includes system logs, operational metrics, and system calls. The model is trained using both contrastive loss and classification loss to improve its ability to detect fine-grained anomaly patterns. The method is evaluated under multiple typical attack types. It is also tested in a simulated real-time data stream scenario to examine its responsiveness. Experimental results show that the proposed method outperforms existing approaches across multiple performance metrics. It demonstrates strong detection accuracy and adaptability, effectively addressing complex anomalies in distributed environments. Through careful design of key modules and optimization of the training mechanism, the proposed method achieves a balance between privacy preservation and detection performance. It offers a feasible technical path for intelligent security management in distributed systems.