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Open access Aug 2026

LBSFL: a lightweight robust federated learning method for IoT

Experiments show that LBSFL achieves competitive model accuracy while substantially reducing computational and communication overhead in most evaluated settings, indicating that LBSFL provides a favorable trade-off between robustness and efficiency for IoT-oriented federated learning.

Wei Ma, Wenjun Tian, Qihang Zhao et al. · 0 citations
Aug 2026

Lightweight and secure federated learning in IoT – A decentralized client selection approach

Results demonstrate the practicality of integrating predictive intelligence and decentralized coordination for scalable and secure FL, and avoids overloading any single node and ensures fault-tolerant, adaptive selection through continuous monitoring and helper-assisted data gathering.

Mohamed Aiche, Samir Ouchani, Hafida Bouarfa · 0 citations
Preprint Aug 2026

FBID: Adaptive Personalized Federated Learning for Robust Out-of-Distribution Attack Detection in IoT Networks

Federated Bandit Intrusion Detection (FBID), a novel adaptive PFL framework to address this limitation through server-side personalization control, employs a contextual multi-armed bandit at the server to dynamically regulate each client's local training intensity according to its observed behavior and update quality.

A. Bui, C. T. Nguyen, Hoang-Anh Pham et al. · 0 citations
Aug 2026

The GAO-based federated learning framework with adaptive client selection for resource-efficient edge-IoT systems

The Federated Green Anaconda Optimizer (FedGAO), an innovative FL framework inspired by the behavioral patterns of the Green Anaconda Optimizer (GAO), is proposed, demonstrating superior performance in terms of accuracy, convergence speed, and resource efficiency.

Elahe Eslami, S. A. Shahzadeh Fazeli, J. Abouei et al. · 0 citations
Open access Aug 2026

Adaptive Hyperparameter Adjustment and Resource Allocation for Federated Learning in the Industrial Internet of Things

This paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks that achieves competitive classification accuracy while reducing single-round training time by up to 70%.

Shuo He, He-Yang Wei, Congxian Bi et al. · 0 citations
#federated learning Open access Sep 2026

FedQS: asynchronous federated learning based on queue scheduling

An asynchronous framework named FedQS, which employs a multi-dimensional staleness evaluation mechanism that dynamically assesses updates by combining the similarity between local and global models with client latency metrics, and implements a decoupling solution via a queue scheduling algorithm to resolve the coupling...

Jia-Hui Zhou, Fang Li, Tian-Yu Shi et al. · 0 citations

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