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federated learning

2,055 papers

#federated learning Preprint Oct 2026

Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation

Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches assume a homogeneous compute budget across institutions, limiting participation of low-resource sites. We propose Fed-ADApt, a depth-adaptive federated framework for UNet...

Abhijeet Parida, Zhi-Fan Jiang, Pooneh Roshanitabrizi et al. · 0 citations
#federated learning Open access Oct 2026

LLM‐Powered Data Synthesis on Blockchain for Fusion Isomerism Learning in Heterogeneous Federated Systems

Severe data starvation, architectural heterogeneity and Byzantine vulnerabilities fundamentally impede the deployment of robust multiclass classification models in decentralised edge environments. To address these intertwined challenges, we propose fusion isomerism learning (FusionIL) , a secure and domain‐agnost...

Zhi-Hao Hao, Long-Bing Cao, Han Yu et al. · 0 citations
#federated learning Open access Oct 2026

Train with Ramps, Deploy with Ranks: code, results and preprint

A median filter removes impulsive noise by sorting a few neighbouring samples and picking the middle one. Filters of this family (rank-order or order-statistic filters) need no multiplications, cannot be thrown off by a few wild samples, and their output never moves more than their input does. They were popular in the...

Juan Carlos del Rio Romero · 0 citations
#federated learning Open access Oct 2026

Rendre effectifs les droits de l'enfant, entre protection et participation

Résumé (FR) Ce document, produit avec l'assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) entrant dans l'état de la technique selon les législations applicables (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Face...

Xavier Pillet · 0 citations
#federated learning Open access Oct 2026

Beyond Diagnostic Accuracy: Calibration and Predictive Uncertainty of Artificial Intelligence Models for Oral and Dental Disease Diagnosis

A Systematic Review Protocol “Beyond Diagnostic Accuracy: Calibration and Predictive Uncertainty of Artificial Intelligence Models for Oral and Dental Disease Diagnosis” Title of the ReviewBeyond Diagnostic Accuracy: Calibration and Predictive Uncertainty of Artificial Intelligence Models for Oral and Dental Disease Di...

Md Rakibul Islam · 0 citations
#federated learning Open access Oct 2026

Train with Ramps, Deploy with Ranks: Tiny, robust rank-order filters that compete with neural networks

A median filter removes impulsive noise by sorting a few neighbouring samples and picking the middle one. Filters of this family (rank-order or order-statistic filters) need no multiplications, cannot be thrown off by a few wild samples, and their output never moves more than their input does. They were popular in the...

Juan Carlos del Rio Romero · 0 citations
#federated learning Open access Oct 2026

Train with Ramps, Deploy with Ranks: code, results and preprint

A median filter removes impulsive noise by sorting a few neighbouring samples and picking the middle one. Filters of this family (rank-order or order-statistic filters) need no multiplications, cannot be thrown off by a few wild samples, and their output never moves more than their input does. They were popular in the...

Juan Carlos del Rio Romero · 0 citations
#federated learning Open access Oct 2026

Re-created code for: Nuclei segmentation and classification from histopathology images using federated learning for end-edge platform

Re-created code for the PLoS One article e0322749 (2025): nuclei segmentation (SegNet) and classification (DenseNet121) on PanNuke with Hyperband tuning, TensorFlow Lite quantization and federated learning. Includes the result tables underlying the reproduced results.

Anjir Ahmed Chowdhury, S M Hasan Mahmud, Md Palash Uddin et al. · 0 citations
#federated learning Open access Oct 2026

Train with Ramps, Deploy with Ranks: Tiny, robust rank-order filters that compete with neural networks

A median filter removes impulsive noise by sorting a few neighbouring samples and picking the middle one. Filters of this family (rank-order or order-statistic filters) need no multiplications, cannot be thrown off by a few wild samples, and their output never moves more than their input does. They were popular in the...

Juan Carlos del Rio Romero · 0 citations
#federated learning Open access Oct 2026

ATLAS: an adaptive threat learning and analysis system using heterogeneous federated deep learning and FedNova aggregation for multi-cloud environments

The proliferation of multi-cloud architectures has intensified challenges in phishing detection, as security intelligence becomes fragmented across heterogeneous cloud infrastructures governed by stringent privacy regulations and data sovereignty constraints. Conventional centralized threat detection paradigms necessit...

Shakir Khan, Arvind Panwar, Achin Jain et al. · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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