Papers Federated Learning
“Federated Learning” 태그가 달린 논문 8,215편 · 필터 해제
vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning
Quantum federated learning (QFL) has emerged as a promising approach for collaboratively training compact quantum neural networks (QNNs) over distributed private data on resource-constrained devices. However, differences…
Neural Architecture SearchFederated LearningFedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices
Federated learning (FL) on heterogeneous edge devices must jointly accommodate unequal resource budgets and domain-shifted local data. Existing resource-adaptive methods decide how much of a model each client trains but …
Federated LearningFedLore: Communication and Memory Efficient Federated Learning via Shared Gradient Low-Rank Projection
Federated training of foundation models is constrained by client memory and communication costs. LoRA-based methods reduce these costs through low-rank adapters, but their fixed rank budget can limit adaptation. Gradient…
Federated LearningFedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantization
Federated Learning (FL) enables privacy-preserving fine-tuning of Large Language Models (LLMs), yet the massive communication overhead remains a critical bottleneck. Furthermore, applying Low-Rank Adaptation (LoRA) in FL…
Federated LearningFedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains
Robust perception in intelligent vehicles demands 3D object detectors that remain dependable under domain shifts, such as changes in time of day, location, or weather. However, due to costly annotation and rare shifts, s…
Federated LearningStructural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation
Federated learning lets multiple participants train a shared model without pooling raw data, by exchanging locally trained model updates instead. Federated averaging assumes that averaging local models is a reasonable wa…
Federated LearningHybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning
Cross-silo Federated Learning (FL) enables geographically distributed institutions to collaboratively train machine learning models without sharing raw data. In wide-area deployments, however, communication delays often …
Federated LearningOmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis
Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with H…
Federated LearningA Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction
Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks, and are they zeroth-order or higher-order? Addressing these questi…
Federated LearningBeyond Conventional Federated Learning via High-Order Regularization
Federated clients that perform several local optimization steps can return parameter displacements with widely different magnitudes. The quadratic regularization of FedProx grows linearly with displacement and therefore …
Federated LearningPrivacy-Preserving Split Learning for Federated LLM Fine-Tuning
Fine-tuning large language models (LLMs) on domain-specific data is essential for downstream adaptation. In many deployments, a participant cannot hold the complete model locally. This happens because the model owner kee…
Federated LearningNEXUS-MI: Communication-Aware Federated Personalization for Gateway-Coordinated Motor-Imagery Brain-Computer Interfaces
Electroencephalography (EEG)-based motor-imagery brain-computer interfaces (MI-BCIs) vary across subjects and sessions, complicating personalization from limited calibration data. Federated learning can exploit shared re…
Federated LearningCascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning
Federated learning shares model updates rather than raw data, yet these updates can be inverted to reconstruct the clients' training data. Analytic reconstruction attacks, which invert a gradient in closed form, degrade …
Federated LearningModality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G
Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots collaborate through low-latency connectivity, edge intelligence, and …
Federated LearningFedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification
Artificial intelligence models are promising for medical diagnosis, but they require large numbers of unbiased data, which in medicine are distributed across hospitals and cannot be centralized to protect patient privacy…
Federated LearningMedical DiagnosisSolution-space heterogeneity shapes federated learning dynamics across partial differential equations
Federated scientific machine learning enables institutions to train neural surrogates without centralizing local physical data, yet studies of partial differential equations (PDEs) lack a transferable definition of non-i…
Federated LearningFederated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates
Detecting orchestrated cyberattack campaigns that span multiple organizations traditionally requires sharing sensitive telemetry and threat intelligence across institutional boundaries and country borders, a barrier that…
Federated LearningResilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning
Due to resource constraints or external and internal uncertainties, clients in real-world federated learning systems are often intermittently available edge devices. In highly dynamic environments, the parameter server l…
Federated LearningQuantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients
Quantum federated learning enables collaborative model training across quantum devices without sharing raw data, and it faces the data and hardware heterogeneity inherent to noisy quantum devices. Utilizing the quantum g…
Federated LearningFedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling
Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling workflows. However, agents deployed at individual hospitals remain …
Federated Learning