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 32013250 of 6771 papers

TitleStatusHype
A Review of Federated Learning in Energy Systems0
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models0
FedFair^3: Unlocking Threefold Fairness in Federated Learning0
Training Fair Models in Federated Learning without Data Privacy Infringement0
FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining0
FedBot: Enhancing Privacy in Chatbots with Federated Learning0
Differentially Private Multi-Site Treatment Effect Estimation0
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation0
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning0
FedBoost: A Communication-Efficient Algorithm for Federated Learning0
FedBone: Towards Large-Scale Federated Multi-Task Learning0
FedFMC: Sequential Efficient Federated Learning on Non-iid Data0
Communication-Efficient and Personalized Federated Lottery Ticket Learning0
FedFm: Towards a Robust Federated Learning Approach For Fault Mitigation at the Edge Nodes0
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning0
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems0
Fed-Focal Loss for imbalanced data classification in Federated Learning0
FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems0
Communication-Efficient and Drift-Robust Federated Learning via Elastic Net0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments0
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain0
FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks0
Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network0
Differentially Private Vertical Federated Learning0
Differentially Private Wireless Federated Learning Using Orthogonal Sequences0
Communication-Efficient Agnostic Federated Averaging0
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning0
FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient0
FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation0
FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design0
FedGA-Tree: Federated Decision Tree using Genetic Algorithm0
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging0
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization0
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models0
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities0
FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion0
FedGen: Generalizable Federated Learning for Sequential Data0
FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning0
A Convergence Theory for Federated Average: Beyond Smoothness0
Fed-Joint: Joint Modeling of Nonlinear Degradation Signals and Failure Events for Remaining Useful Life Prediction using Federated Learning0
Communication Efficient Adaptive Model-Driven Quantum Federated Learning0
FedGL: Federated Graph Learning Framework with Global Self-Supervision0
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation0
FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions0
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation0
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning0
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout0
Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning0
A Real-time Contribution Measurement Method for Participants in 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