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 31013150 of 6771 papers

TitleStatusHype
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation0
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT0
Federated Learning for Metaverse: A Survey0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles0
Federated Learning for Medical Image Analysis: A Survey0
Federated Self-supervised Speech Representations: Are We There Yet?0
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning0
DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology0
Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks0
Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan0
Accelerating Split Federated Learning over Wireless Communication Networks0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark0
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions0
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning0
Federated Sequence-to-Sequence Learning for Load Disaggregation from Unbalanced Low-Resolution Smart Meter Data0
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients0
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method0
Federated Sinkhorn0
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data0
Federated Smoothing Proximal Gradient for Quantile Regression with Non-Convex Penalties0
Federated Social Recommendation with Graph Neural Network0
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data0
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework0
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields0
D2p-fed:Differentially Private Federated Learning with Efficient Communication0
Federated Split BERT for Heterogeneous Text Classification0
Federated Split Learning for Human Activity Recognition with Differential Privacy0
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks0
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis0
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis0
Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training0
Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities0
Federated Learning for Heterogeneous Bandits with Unobserved Contexts0
Federated Stochastic Gradient Descent Begets Self-Induced Momentum0
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach0
Federated Structured Sparse PCA for Anomaly Detection in IoT Networks0
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning0
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression0
ATM: Improving Model Merging by Alternating Tuning and Merging0
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling0
Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning0
Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation0
Federated Time Series Generation on Feature and Temporally Misaligned Data0
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets0
Federated Transfer Learning Aided Interference Classification in GNSS Signals0
Federated Transfer Learning: concept and applications0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions0
Federated Transfer Learning with Differential Privacy0
Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning0
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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