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.