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 33013350 of 6771 papers

TitleStatusHype
Strategic Federated Learning: Application to Smart Meter Data Clustering0
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination0
Federated Learning for Diffusion Models0
Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions0
Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning0
FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy0
Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives0
Cross-Silo Federated Learning: Challenges and Opportunities0
A Systematic Literature Review on Client Selection in Federated Learning0
Federated Learning for Data and Model Heterogeneity in Medical Imaging0
Federated Learning for Cyber Physical Systems: A Comprehensive Survey0
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration0
Federated Learning for Cross-block Oil-water Layer Identification0
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses0
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data0
Layer-wise and Dimension-wise Locally Adaptive Federated Learning0
Fed-LDR: Federated Local Data-infused Graph Creation with Node-centric Model Refinement0
A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles0
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning0
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks0
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration0
Cross-Modal Vertical Federated Learning for MRI Reconstruction0
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks0
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges0
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality0
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients0
Federated Learning for Computer Vision0
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey0
Digital Over-the-Air Federated Learning in Multi-Antenna Systems0
Federated Neuro-Symbolic Learning0
FedLog: Personalized Federated Classification with Less Communication and More Flexibility0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning0
Federated Learning for Commercial Image Sources0
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior0
Heterogeneous Federated Learning with Splited Language Model0
Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation0
Federated Learning for Coalition Operations0
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization0
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder0
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning0
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches0
Cross-Fusion Rule for Personalized Federated Learning0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism0
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization0
Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO0
Cross-domain Federated Object Detection0
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization0
Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning0
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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