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 25512600 of 6771 papers

TitleStatusHype
Personalized Federated Learning with Contextual Modulation and Meta-LearningCode0
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading0
Personalized Federated Learning with Attention-based Client Selection0
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise0
Federated Learning with Projected Trajectory Regularization0
Holistic analysis on the sustainability of Federated Learning across AI product lifecycle0
An effective and efficient green federated learning method for one-layer neural networksCode0
Federated Learning via Input-Output Collaborative DistillationCode1
Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning0
Sparse Training for Federated Learning with Regularized Error Correction0
DCFL: Non-IID awareness Data Condensation aided Federated Learning0
Multimodal Federated Learning with Missing Modality via Prototype Mask and ContrastCode0
Federated Continual Novel Class Learning0
Federated Quantum Long Short-term Memory (FedQLSTM)0
Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron ProvenanceCode1
Towards Fair Graph Federated Learning via Incentive MechanismsCode1
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection0
Enhancing Neural Training via a Correlated Dynamics Model0
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization0
Federated Learning with Extremely Noisy Clients via Negative DistillationCode1
FedSODA: Federated Cross-assessment and Dynamic Aggregation for Histopathology SegmentationCode0
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation NoiseCode1
Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing0
Blood Glucose Level Prediction: A Graph-based Explainable Method with Federated Learning0
On the Role of Server Momentum in Federated Learning0
SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable MasksCode0
Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective0
Decentralised and collaborative machine learning framework for IoT0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs0
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
Federated Multi-View Synthesizing for Metaverse0
Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated LearningCode0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks0
Online Vertical Federated Learning for Cooperative Spectrum Sensing0
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants0
A review of federated learning in renewable energy applications: Potential, challenges, and future directions0
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices0
MISA: Unveiling the Vulnerabilities in Split Federated Learning0
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion0
Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data0
PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated LearningCode0
Take History as a Mirror in Heterogeneous Federated Learning0
Federated Learning with Instance-Dependent Noisy Label0
Value of Information and Timing-aware Scheduling for Federated Learning0
Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning0
A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease0
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive SpaceCode0
Show:102550
← PrevPage 52 of 136Next →

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