SOTAVerified

Federated Learning

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.

Papers

Showing 30013050 of 6771 papers

TitleStatusHype
Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies0
Federated Learning for Sparse Principal Component Analysis0
Federated X-armed Bandit with Flexible Personalisation0
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications0
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding0
Federated Learning for Smart Healthcare: A Survey0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey0
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach0
Federated Mixture of Experts0
Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities0
Federated Learning for Short Text Clustering0
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach0
Federated Model Heterogeneous Matryoshka Representation Learning0
Federated Learning for Short-term Residential Load Forecasting0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization0
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing0
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management0
Federated Multi-Agent Mapping for Planetary Exploration0
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages0
Federated Multi-Armed Bandits Under Byzantine Attacks0
Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks0
Federated Multilinear Principal Component Analysis with Applications in Prognostics0
Federated Multilingual Models for Medical Transcript Analysis0
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art0
Federated Learning for Ranking Browser History Suggestions0
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information0
Age-of-Gradient Updates for Federated Learning over Random Access Channels0
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks0
Federated Multi-Target Domain Adaptation0
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning0
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning0
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey0
Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications0
Federated Multi-view Matrix Factorization for Personalized Recommendations0
Federated Multi-View Synthesizing for Metaverse0
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems0
Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion0
Federated Learning for Physical Layer Design0
Federated Learning for Open Banking0
Federated Neural Architecture Search with Model-Agnostic Meta Learning0
Federated Neural Collaborative Filtering0
Federated Neural Compression Under Heterogeneous Data0
Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update0
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain0
Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning0
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment0
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing0
Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75Unverified
2SiloBN + SAMmIoU49.1Unverified
3SiloBNmIoU45.96Unverified
4FedSAM + SWAmIoU43.42Unverified
5FedASAM + SWAmIoU43.02Unverified
6FedAvg + SWAmIoU42.48Unverified
7FedASAMmIoU42.27Unverified
8FedSAMmIoU41.22Unverified
9FedAvgmIoU38.65Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32Unverified
2FedSAM + SWAAcc@1-1262Clients68.12Unverified
3FedAvg + SWAAcc@1-1262Clients67.52Unverified
4FedASAMAcc@1-1262Clients64.23Unverified
5FedSAMAcc@1-1262Clients63.72Unverified
6FedAvgAcc@1-1262Clients61.91Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64Unverified
2FedASAMACC@1-100Clients39.76Unverified
3FedSAM + SWAACC@1-100Clients39.51Unverified
4FedSAMACC@1-100Clients36.93Unverified
5FedAvgACC@1-100Clients36.74Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62Unverified
2FedASAMACC@1-100Clients40.81Unverified
3FedSAM + SWAACC@1-100Clients39.24Unverified
4FedAvgACC@1-100Clients38.59Unverified
5FedSAMACC@1-100Clients38.56Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72Unverified
2FedSAM + SWAACC@1-100Clients46.76Unverified
3FedASAMACC@1-100Clients46.58Unverified
4FedSAMACC@1-100Clients44.84Unverified
5FedAvgACC@1-100Clients41.27Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27Unverified
2FedASAMACC@1-100Clients47.78Unverified
3FedSAM + SWAACC@1-100Clients46.47Unverified
4FedSAMACC@1-100Clients46.05Unverified
5FedAvgACC@1-100Clients42.17Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17Unverified
2FedSAM + SWAACC@1-100Clients47.96Unverified
3FedASAMACC@1-100Clients45.61Unverified
4FedSAMACC@1-100Clients44.73Unverified
5FedAvgACC@1-100Clients40.43Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01Unverified
2FedSAM + SWAACC@1-100Clients39.3Unverified
3FedASAMACC@1-100Clients36.04Unverified
4FedSAMACC@1-100Clients31.04Unverified
5FedAvgACC@1-100Clients30.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97Unverified
2FedASAM + SWAACC@1-100Clients54.79Unverified
3FedSAM + SWAACC@1-100Clients53.67Unverified
4FedSAMACC@1-100Clients53.39Unverified
5FedAvgACC@1-100Clients50.25Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5Unverified
2FedSAM + SWAACC@1-100Clients54.36Unverified
3FedASAM + SWAACC@1-100Clients54.1Unverified
4FedSAMACC@1-100Clients53.97Unverified
5FedAvgACC@1-100Clients50.66Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81Unverified
2FedSAMACC@1-100Clients54.01Unverified
3FedSAM + SWAACC@1-100Clients53.9Unverified
4FedASAM + SWAACC@1-100Clients53.86Unverified
5FedAvgACC@1-100Clients49.92Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2Unverified