International Conference on Computer and Knowledge Engineering

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Robust Distributed Learning over Heterogeneous Adaptive Networks based on Federated BSP Model

Authors :

Fatemeh Barani¹ MohammadHafez Yari² Abdorreza Savadi³ Hadi Sadoghi Yazdi⁴

1- Department of Computer Engineering, Higher Education Complex of Bam, Bam, Iran 2- Department of Computer Engineering Ferdowsi University of Mashhad Mashhad, Iran 3- Department of Computer Engineering Ferdowsi University of Mashhad Mashhad, Iran 4- Department of Computer Engineering Ferdowsi University of Mashhad Mashhad, Iran

Keywords :

Federated Bulk Synchronous Parallel (FBSP)،Robust distributed learning،Parallel computing models،Straggler Problem،Distributed adaptive networks

Abstract :

Distributed adaptive networks provide a scalable, fault-tolerant, and decentralized platform for distributed learning. Effective learning in such networks requires a structured approach based on a parallel computing model to ensure smooth interaction between parallel software and hardware. While the bulk synchronous parallel (BSP) model can be applied, in heterogeneous environments it suffers from the straggler problem caused by global barrier synchronization, increasing node idle time. This paper introduces FBSP (Federated Bulk Synchronous Parallel), an enhanced BSP model designed for heterogeneous adaptive networks. FBSP uses content–structural clustering to group nodes into regions and employs a two-level barrier synchronization—regional and global—to optimize computational flow, thereby reducing communication overhead and idle time.

Papers List

List of archived papers

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A Genetic-based Fusion Approach of Persian and Universal Phonetic results for Spoken Language Identification

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FAST: FPGA Acceleration of Neural Networks Training

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Enhanced Melanoma Detection: An Improved Deformable DETR Model with Efficient Channel Attention

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Decentralized Federated Learning in IoT Environments: A Hierarchical Approach

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Adaptive Ensemble Learning for Software Defect Prediction: A Dynamic Weighted Hybrid Model Using SVM, DT, and ANFIS-PSO

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AL-YOLO: Accurate and Lightweight Vehicle and Pedestrian Detector in Foggy Weather

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Using Deep Learning for Classification of Lung Cancer on CT Images in Ardabil Province

Mohammad Ali Javadzadeh Barzaki - Jafar Abdollahi - Mohammad Negaresh - Maryam Salimi - Hadi Zolfeghari - Mohsen Mohammadi - Asma Salmani - Rona Jannati - Firouz Amani

AIRSPAN-X: Federated XGBoost with Sequential Anomaly Detection for Explainable Urban Air Quality Prediction

Saghar Shafaati - S. Hossein Erfani