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

Algorithms, Journal Year: 2024, Volume and Issue: 17(4), P. 160 - 160

Published: April 15, 2024

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

Language: Английский

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Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

International Journal of Communication Systems, Journal Year: 2024, Volume and Issue: 37(11)

Published: May 9, 2024

Summary In wireless sensor networks (WSNs), target tracking has been prominently raised in recent days. Because of the frequent utilization WSN, attention on is greatly increased. The estimated optimal value derived from earlier moment rarely taken into consideration traditional target‐tracking algorithms. One most crucial uses WSNs mobile tracking, and it especially used for spying. Precision surveillance heavily dependent localization or distance estimation, extensive study done this area. This research aims to develop a new network‐assisted movement prediction model WSN with reduced energy consumption. major phases involved proposed are (a) mobility (b) prediction. Initially, help adaptive distributed extended Kalman filtering (ADEKF). performance improved by optimally tuning parameters ADEKF support squid game optimizer (ISGO). Then, phase executed input like “Angle Arrival (AoA) Received Signal Strength (RSS),” progress node predicted. implementation outcome validated concerning various metrics. Overall analysis shows that developed offers 2.5% terms RMSE measures. better while validating existing approaches.

Language: Английский

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AAMR-FCN myeloma cancer net: Adaptive and attention-based mask R-FCN for diagnosing myeloma cancer using cell microscopic images with hybrid heuristic strategy

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 106987 - 106987

Published: Oct. 10, 2024

Language: Английский

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An Improved Spider Wasp Optimizer for UAV Three-Dimensional Path Planning

Biomimetics, Journal Year: 2024, Volume and Issue: 9(12), P. 765 - 765

Published: Dec. 16, 2024

This paper proposes an Improved Spider Wasp Optimizer (ISWO) to address inaccuracies in calculating the population (N) during iterations of SWO algorithm. By innovating iteration formula and integrating advantages Differential Evolution Crayfish Optimization Algorithm, along with introducing opposition-based learning strategy, ISWO accelerates convergence. The adaptive parameters trade-off probability (TR) crossover (Cr) are dynamically updated balance exploration exploitation phases. In each generation, optimizes individual positions using Lévy flights, DE’s mutation, operations, COA’s update mechanisms. OBL strategy is applied every 10 generations enhance diversity. As progress, size gradually decreases, ultimately yielding optimal solution recording convergence process. algorithm’s performance tested 2017 test set, modeling a mountainous environment Gaussian function model. Under constraint conditions, objective establish mathematical model for UAV flight. minimal cost obstacle-avoiding flight within specified airspace obtained fitness function, path smoothed through cubic spline interpolation. Overall, generates high-quality, smooth paths fewer iterations, overcoming premature insufficient local search capabilities traditional genetic algorithms, adapting complex terrains, providing efficient reliable solution.

Language: Английский

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A Survey on Securing 6G Wireless Communications based Optimization Techniques

2022 International Wireless Communications and Mobile Computing (IWCMC), Journal Year: 2023, Volume and Issue: unknown

Published: June 19, 2023

The increasing number of applications and devices in the Sixth-generation (6G) networks diversity mobile data, architectures, technologies make security privacy a critical concern. Advanced metaheuristics algorithms (MHAs) have recently become viable solution for optimizing wireless networks, combining game theory convex optimization, several other advanced models. As subfield Artificial Intelligence (AI), MHAs are inspired by concepts from Evolutionary Algorithms (EAs), Trajectory-based (TAs), Swarm (SI). Recent implementations 6G effectively solved complex problems. This study examines MHAs' utilization addressing challenges networks. paper provides comprehensive overview their use solving problems 6G. current limitations literature also identified, avenues further research suggested. reader will clear image needed tools securing using MHAs.

Language: Английский

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