# FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing

arXiv: https://arxiv.org/abs/2205.11519 · HTML: https://mlsanity.com/paper/2205.11519/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [Federated Learning for Malware Detection in IoT Devices](https://mlsanity.com/paper/2104.09994/) (2021)
- [Gradual Federated Learning with Simulated Annealing](https://mlsanity.com/paper/2110.05178/) (2021)

## Built on by (most cited first, 1 of 1)

- [FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification](https://mlsanity.com/paper/2609.05223/) (2026)

## Abstract (arXiv)

Fast identification of new network attack patterns is crucial for improving network security. Nevertheless, identifying an ongoing attack in a heterogeneous network is a non-trivial task. Federated learning emerges as a solution to collaborative training for an Intrusion Detection System (IDS). The federated learning-based IDS trains a global model using local machine learning models provided by federated participants without sharing local data. However, optimization challenges are intrinsic to federated learning. This paper proposes the Federated Simulated Annealing (FedSA) metaheuristic to select the hyperparameters and a subset of participants for each aggregation round in federated learning. FedSA optimizes hyperparameters linked to the global model convergence. The proposal reduces aggregation rounds and speeds up convergence. Thus, FedSA accelerates learning extraction from local models, requiring fewer IDS updates. The proposal assessment shows that the FedSA global model converges in less than ten communication rounds. The proposal requires up to 50% fewer aggregation rounds to achieve approximately 97% accuracy in attack detection than the conventional aggregation approach.