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Federated Learning
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Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.
This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model
Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
Federated Learning for Commercial Image Sources
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraints
FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks
Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
Safeguarding Federated Learning-based Road Condition Classification
Federated Learning in Open- and Closed-Loop EMG Decoding: A Privacy and Performance Perspective
Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs
Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Convergence of Agnostic Federated Averaging
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation
Lightweight Federated Learning over Wireless Edge Networks
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences
Federated Learning with Graph-Based Aggregation for Traffic Forecasting
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise
SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Model Parallelism With Subnetwork Data Parallelism
Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0
HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric
Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
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