Towards optimal neighbour selection mechanisms in peer-to-peer networks

Zhenying, Yang

doi:10.1007/s12596-023-01373-w

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…Simultaneously, the utilization of the Particle Swarm Optimization (PSO) algorithm in distributed environments has gained traction. Various PSO algorithms, including dynamic multi-swarm PSO (DMS-PSO), PSO for neighbor selection in peer-to-peer (P2P) networks, and gossip-based PSO for maintaining flexible P2P networks, have been explored [38,39]. Studies have demonstrated the adaptability of PSO in diverse distributed environments, showcasing its potential in addressing optimization challenges.…”

Section: Related Workmentioning

confidence: 99%

A Communication-Efficient Federated Learning Framework for Sustainable Development Using Lemurs Optimizer

Al-Betar,

Abasi,

Alyasseri

et al. 2024

Algorithms

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The pressing need for sustainable development solutions necessitates innovative data-driven tools. Machine learning (ML) offers significant potential, but faces challenges in centralized approaches, particularly concerning data privacy and resource constraints in geographically dispersed settings. Federated learning (FL) emerges as a transformative paradigm for sustainable development by decentralizing ML training to edge devices. However, communication bottlenecks hinder its scalability and sustainability. This paper introduces an innovative FL framework that enhances communication efficiency. The proposed framework addresses the communication bottleneck by harnessing the power of the Lemurs optimizer (LO), a nature-inspired metaheuristic algorithm. Inspired by the cooperative foraging behavior of lemurs, the LO strategically selects the most relevant model updates for communication, significantly reducing communication overhead. The framework was rigorously evaluated on CIFAR-10, MNIST, rice leaf disease, and waste recycling plant datasets representing various areas of sustainable development. Experimental results demonstrate that the proposed framework reduces communication overhead by over 15% on average compared to baseline FL approaches, while maintaining high model accuracy. This breakthrough extends the applicability of FL to resource-constrained environments, paving the way for more scalable and sustainable solutions for real-world initiatives.

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Section: Related Workmentioning

confidence: 99%