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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1901–1950 of 6771 papers

TitleStatusHype
Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks—0
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning—0
Accelerating Split Federated Learning over Wireless Communication Networks—0
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
EM for Mixture of Linear Regression with Clustered Data—0
EMO: Edge Model Overlays to Scale Model Size in Federated Learning—0
Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs—0
Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI—0
Empirical Studies of Institutional Federated Learning For Natural Language Processing—0
Employing Federated Learning for Training Autonomous HVAC Systems—0
Employing Layerwised Unsupervised Learning to Lessen Data and Loss Requirements in Forward-Forward Algorithms—0
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers—0
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
Empowering Federated Learning for Massive Models with NVIDIA FLARE—0
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics—0
Evidential Federated Learning for Skin Lesion Image Classification—0
Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning—0
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning—0
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data—0
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision—0
Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective—0
Enabling On-Device Training of Speech Recognition Models with Federated Dropout—0
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications—0
Enabling SQL-based Training Data Debugging for Federated Learning—0
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness—0
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework—0
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters—0
Encoded Spatial Attribute in Multi-Tier Federated Learning—0
Can collaborative learning be private, robust and scalable?—0
D2p-fed:Differentially Private Federated Learning with Efficient Communication—0
End-to-End on-device Federated Learning: A case study—0
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning—0
Can Fair Federated Learning reduce the need for Personalisation?—0
ATM: Improving Model Merging by Alternating Tuning and Merging—0
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation—0
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
Energy-Aware Federated Learning in Satellite Constellations—0
Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA—0
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks—0
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design—0
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach—0
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks—0
Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning—0
Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks—0
Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks—0
Energy Efficient Federated Learning Over Wireless Communication Networks—0
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning—0
A Thorough Assessment of the Non-IID Data Impact in Federated Learning—0
Show:102550
← PrevPage 39 of 136Next →
All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified