federated learning

Distributed Learning with Selective State Space Models: Architecture-Aware Convergence Analysis

Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to transformers by combining linear-time sequence modeling with recurrent state-space dynamics. However, the behavior of SSMs in distributed learning settings remains poorly understood. In particular, the existing standard federated lear...

Artificial Intelligence-Driven Theranostics: Integrating Machine Learning For Precision Diagnosis And Personalized Therapy

Theranostics links disease characterization with treatment selection and response assessment, creating a framework in which diagnostic information directly informs individualized therapy. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning, radiomics, radiogenomics, and multimodal data...

EduSense - A Real-Time IoT-Augmented Explainable Ensemble Learning Framework With Face-Verified Attendance For Early Academic Risk Detection In Engineering Education

Engineering colleges across India face a critical challenge, with approximately 28% of enrolled students failing to complete their degrees on time. In regions like Vidarbha, Maharashtra, this rate is exacerbated by localized financial stress, first-generation academic backgrounds, and constrained support systems. Tradi...

An Adaptive and Interpretable Federated Deep Learning Architecture for Privacy-Aware Network Threat Classification

The increasing distribution of enterprise computing across cloud platforms, edge devices, branch networks, and Internet-of-Things environments has made the collection and centralized analysis of network telemetry increasingly difficult. Conventional deep-learning intrusion detection approaches commonly require the move...

An adaptive federated few-shot learning method with intelligent device selection

Federated learning, which enables the training of intelligent models without transferring data and solely by relying on the computational capabilities of distributed devices, faces significant challenges in few-shot scenarios due to data heterogeneity, resource constraints, and slow convergence. The primary obstacle li...

EduSense - A Real-Time IoT-Augmented Explainable Ensemble Learning Framework With Face-Verified Attendance For Early Academic Risk Detection In Engineering Education

Engineering colleges across India face a critical challenge, with approximately 28% of enrolled students failing to complete their degrees on time. In regions like Vidarbha, Maharashtra, this rate is exacerbated by localized financial stress, first-generation academic backgrounds, and constrained support systems. Tradi...

Artificial Intelligence-Driven Theranostics: Integrating Machine Learning For Precision Diagnosis And Personalized Therapy

Theranostics links disease characterization with treatment selection and response assessment, creating a framework in which diagnostic information directly informs individualized therapy. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning, radiomics, radiogenomics, and multimodal data...

An Adaptive and Interpretable Federated Deep Learning Architecture for Privacy-Aware Network Threat Classification

The increasing distribution of enterprise computing across cloud platforms, edge devices, branch networks, and Internet-of-Things environments has made the collection and centralized analysis of network telemetry increasingly difficult. Conventional deep-learning intrusion detection approaches commonly require the move...

song1741008932-ship-it/FedDWM: v1.2

FedDWM: A Dynamic Watermark Migration Framework for Model Poisoning Defense in Federated Learning

The Role of Smart Systems and Machine Learning in Business Decision-Making: A Bibliometric Review

The Changing Landscape of Plant Disease Detection: The Role of Artificial Intelligence

Plant diseases constrain agricultural productivity and food security, while conventional diagnosis depends heavily on visible symptoms and specialist expertise, limiting rapid and scalable assessment across heterogeneous agricultural environments. Artificial intelligence (AI), particularly machine learning, deep learni...

Quantum-Enhanced Federated Learning Framework for Next-Generation AIoT Systems: Bridging Neuromorphic Computing and Environmental Intelligence