Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection

• URL: http://arxiv.org/abs/2503.15448v1
• Date: Wed, 19 Mar 2025 17:29:21 GMT
• Title: Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
• Authors: William Marfo, Deepak Tosh, Shirley Moore, Joshua Suetterlein, Joseph Manzano,
• Abstract summary: Communication overhead in federated learning (FL) poses a significant challenge for network anomaly detection systems.<n>This paper presents an adaptive FL framework combining batch size optimization, client selection, and asynchronous updates for efficient anomaly detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Communication overhead in federated learning (FL) poses a significant challenge for network anomaly detection systems, where diverse client configurations and network conditions impact efficiency and detection accuracy. Existing approaches attempt optimization individually but struggle to balance reduced overhead with performance. This paper presents an adaptive FL framework combining batch size optimization, client selection, and asynchronous updates for efficient anomaly detection. Using UNSW-NB15 for general network traffic and ROAD for automotive networks, our framework reduces communication overhead by 97.6% (700.0s to 16.8s) while maintaining comparable accuracy (95.10% vs. 95.12%). The Mann-Whitney U test confirms significant improvements (p < 0.05). Profiling analysis reveals efficiency gains via reduced GPU operations and memory transfers, ensuring robust detection across varying client conditions.

Related papers

• Efficient Denial of Service Attack Detection in IoT using Kolmogorov-Arnold Networks [22.036794530902608]
  This paper introduces a novel lightweight approach to DoS attack detection based on Kolmogorov-Arnold Networks (KANs)<n>KAN achieves state-of-the-art detection performance while maintaining minimal resource requirements.<n>Compared to existing solutions, KAN reduces memory requirements by up to 98% while maintaining competitive detection rates.
  arXiv  Detail & Related papers  (2025-02-03T21:19:46Z)
• Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case [0.30723404270319693]
  This paper introduces a client selection framework for Federated Learning (FL) that incorporates differential privacy and fault tolerance.<n>Results demonstrate up to a 7% improvement in accuracy and a 25% reduction in training time compared to the FedL2P approach.
  arXiv  Detail & Related papers  (2025-01-25T02:50:46Z)
• Efficient Client Selection in Federated Learning [0.30723404270319693]
  Federated Learning (FL) enables decentralized machine learning while preserving data privacy.<n>This paper proposes a novel client selection framework that integrates differential privacy and fault tolerance.
  arXiv  Detail & Related papers  (2025-01-25T02:43:55Z)
• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning [57.38427653043984]
  Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients. We introduce FedCAda, an innovative federated client adaptive algorithm designed to tackle this challenge. We demonstrate that FedCAda outperforms the state-of-the-art methods in terms of adaptability, convergence, stability, and overall performance.
  arXiv  Detail & Related papers  (2024-05-20T06:12:33Z)
• Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization [71.87335804334616]
  Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. The training process of Large Language Models (LLMs) generally incurs the update of significant parameters. This paper proposes an efficient partial prompt tuning approach to improve performance and efficiency simultaneously.
  arXiv  Detail & Related papers  (2023-10-23T16:37:59Z)
• FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data [54.81695390763957]
  Federated learning is an emerging distributed machine learning method. We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate. We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
  arXiv  Detail & Related papers  (2023-09-18T12:35:05Z)
• HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association [38.99309610943313]
  Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients. For many existing FL systems, clients need to frequently exchange model parameters of large data size with the remote cloud server directly via wide-area networks (WAN) We resort to the hierarchical federated learning paradigm of HiFL, which reaps the benefits of mobile edge computing.
  arXiv  Detail & Related papers  (2023-01-16T14:39:04Z)
• Dynamic Attention-based Communication-Efficient Federated Learning [85.18941440826309]
  Federated learning (FL) offers a solution to train a global machine learning model. FL suffers performance degradation when client data distribution is non-IID. We propose a new adaptive training algorithm $textttAdaFL$ to combat this degradation.
  arXiv  Detail & Related papers  (2021-08-12T14:18:05Z)
• CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning [2.2172881631608456]
  We introduce a new family of Federated Learning algorithms called CatFedAvg. It improves the communication efficiency but improves the quality of learning using a category coverage inNIST strategy. Our experiments show that an increase of 10% absolute points accuracy using the M dataset with 70% absolute points lower network transfer over FedAvg.
  arXiv  Detail & Related papers  (2020-11-14T06:52:02Z)
• Toward fast and accurate human pose estimation via soft-gated skip connections [97.06882200076096]
  This paper is on highly accurate and highly efficient human pose estimation. We re-analyze this design choice in the context of improving both the accuracy and the efficiency over the state-of-the-art. Our model achieves state-of-the-art results on the MPII and LSP datasets.
  arXiv  Detail & Related papers  (2020-02-25T18:51:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.