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 53515400 of 6771 papers

TitleStatusHype
How to Backdoor HyperNetwork in Personalized Federated Learning?0
Fairness in Federated Learning for Spatial-Temporal Applications0
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks0
Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection0
Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning0
Federated Continual Learning for Socially Aware Robotics0
Jamming Attacks on Federated Learning in Wireless Networks0
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits0
FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks0
An Interpretable Federated Learning-based Network Intrusion Detection Framework0
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning0
A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction0
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems0
LoMar: A Local Defense Against Poisoning Attack on Federated Learning0
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement0
Fair and efficient contribution valuation for vertical federated learning0
Multi-Model Federated Learning0
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion TheoryCode0
Federated Optimization of Smooth Loss Functions0
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of RobustnessCode0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management0
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection0
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones0
Learning To Collaborate in Decentralized Learning of Personalized Models0
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework0
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning0
Feature-context driven Federated Meta-Learning for Rare Disease Prediction0
Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning0
Federated Learning for Cross-block Oil-water Layer Identification0
Robust Convergence in Federated Learning through Label-wise Clustering0
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit FeedbackCode0
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers0
SPIDER: Searching Personalized Neural Architecture for Federated Learning0
Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity0
Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels0
Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)0
Towards Federated Learning on Time-Evolving Heterogeneous Data0
Faster Rates for Compressed Federated Learning with Client-Variance Reduction0
Sparsified Secure Aggregation for Privacy-Preserving Federated Learning0
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation0
FLoBC: A Decentralized Blockchain-Based Federated Learning Framework0
Distributed Machine Learning and the Semblance of Trust0
Hierarchical Over-the-Air Federated Edge Learning0
On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge0
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling0
FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence0
Certified Federated Adversarial Training0
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity0
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction0
Show:102550
← PrevPage 108 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