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 23012350 of 6771 papers

TitleStatusHype
SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks0
Masked Random Noise for Communication Efficient Federated LearningCode0
FedBAT: Communication-Efficient Federated Learning via Learnable BinarizationCode0
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic SurgeryCode0
Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application0
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG0
Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense0
Strategic Federated Learning: Application to Smart Meter Data Clustering0
Model Hijacking Attack in Federated Learning0
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning0
Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning0
TreeCSS: An Efficient Framework for Vertical Federated Learning0
Load Balancing in Federated Learning0
Mobility-Aware Federated Self-supervised Learning in Vehicular Network0
Algorithms for Collaborative Machine Learning under Statistical Heterogeneity0
FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication0
FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy InsightsCode0
CELLM: An Efficient Communication in Large Language Models Training for Federated Learning0
Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing0
FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models0
Federated Learning based Latent Factorization of Tensors for Privacy-Preserving QoS Prediction0
F-KANs: Federated Kolmogorov-Arnold NetworksCode0
UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification TasksCode0
Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering0
A collaborative ensemble construction method for federated random forest0
Reducing Spurious Correlation for Federated Domain Generalization0
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps0
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning0
FLUE: Federated Learning with Un-Encrypted model weights0
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification0
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction0
SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
Sparse Incremental Aggregation in Multi-Hop Federated Learning0
Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future0
Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review0
HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imaging0
Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets0
FADAS: Towards Federated Adaptive Asynchronous OptimizationCode0
SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices0
A Hybrid Federated Kernel Regularized Least Squares Algorithm0
Spiking Neural Networks in Vertical Federated Learning: Performance Trade-offs0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes0
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and FairnessCode0
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation0
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory0
Federated Learning for Face Recognition via Intra-subject Self-supervised Learning0
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning0
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization0
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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