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 24012450 of 6771 papers

TitleStatusHype
DistDD: Distributed Data Distillation Aggregation through Gradient Matching0
Better Generative Replay for Continual Federated Learning0
Disentangling data distribution for Federated Learning0
Beta Thalassemia Carriers detection empowered federated Learning0
A Multivocal Literature Review on Privacy and Fairness in Federated Learning0
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring0
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks0
Direct Federated Neural Architecture Search0
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning0
Adaptive Scheduling for Machine Learning Tasks over Networks0
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models0
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning0
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning0
Benchmarking Mutual Information-based Loss Functions in Federated Learning0
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation0
Digital versus Analog Transmissions for Federated Learning over Wireless Networks0
Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data0
A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification0
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning0
Digital Ethics in Federated Learning0
Benchmarking Federated Machine Unlearning methods for Tabular Data0
Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learning0
Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations0
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications0
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks0
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks0
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation0
Benchmarking FedAvg and FedCurv for Image Classification Tasks0
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection0
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates0
Differential Privacy Meets Federated Learning under Communication Constraints0
Differentially-Private Multi-Tier Federated Learning0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation0
Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation0
AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling0
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning0
Differential Privacy-enabled Federated Learning for Sensitive Health Data0
Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction0
Differentially Private Wireless Federated Learning Using Orthogonal Sequences0
Differentially Private Vertical Federated Learning0
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning0
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning0
Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning0
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications0
(Amplified) Banded Matrix Factorization: A unified approach to private training0
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks0
Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channels0
Differentially Private Online Federated Learning with Correlated Noise0
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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