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
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning0
Non-Federated Multi-Task Split Learning for Heterogeneous Sources0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning0
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity0
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search0
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated LearningCode0
Sheaf HyperNetworks for Personalized Federated Learning0
Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting0
Gradient Inversion of Federated Diffusion Models0
On Vessel Location Forecasting and the Effect of Federated LearningCode0
Federated Learning with Multi-resolution Model Broadcast0
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning0
Federated and Transfer Learning for Cancer Detection Based on Image Analysis0
SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning0
subMFL: Compatiple subModel Generation for Federated Learning in Device Heterogenous EnvironmentCode0
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective0
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense0
Optimizing Split Points for Error-Resilient SplitFed Learning0
Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain0
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
LoByITFL: Low Communication Secure and Private Federated Learning0
Federated Learning under Partially Class-Disjoint Data via Manifold ReshapingCode0
Differentially Private Clustered Federated Learning0
An Innovative Networks in Federated Learning0
PeerFL: A Simulator for Peer-to-Peer Federated Learning at ScaleCode0
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing0
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization0
Decentralized Directed Collaboration for Personalized Federated Learning0
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated LearningCode0
LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation0
Efficient Model Compression for Hierarchical Federated Learning0
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous ClientsCode0
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation0
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach0
A Systematic Review of Federated Generative Models0
Machine learning in business process management: A systematic literature review0
Multi-Level Additive Modeling for Structured Non-IID Federated LearningCode0
Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client IndexingCode0
FedSheafHN: Personalized Federated Learning on Graph-structured Data0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection0
CAFe: Cost and Age aware Federated Learning0
Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification0
RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure AggregationCode0
Decaf: Data Distribution Decompose Attack against Federated Learning0
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning0
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated LearningCode0
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler0
Towards Client Driven Federated Learning0
Exploring Age-of-Information Weighting in Federated Learning under Data Heterogeneity0
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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