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 21512200 of 6771 papers

TitleStatusHype
Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices0
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model0
Brain Tumor Detection in MRI Based on Federated Learning with YOLOv110
An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms0
Eavesdrop the Composition Proportion of Training Labels in Federated Learning0
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning0
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification0
A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks0
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning0
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions0
Abnormal Client Behavior Detection in Federated Learning0
Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction0
Algorithms for Collaborative Machine Learning under Statistical Heterogeneity0
Federated Learning in Mobile Edge Networks: A Comprehensive Survey0
Brain MRI Screening Tool with Federated Learning0
E2FL: Equal and Equitable Federated Learning0
An Empirical Evaluation of Federated Contextual Bandit Algorithms0
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset0
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning0
BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare0
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning0
Boosting with Multiple Sources0
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack0
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives0
Dynamic Network-Assisted D2D-Aided Coded Distributed Learning0
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration0
Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes0
Dynamic Gradient Aggregation for Federated Domain Adaptation0
Adaptive Gradient Clipping for Robust Federated Learning0
Dynamic Fusion based Federated Learning for COVID-19 Detection0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations0
DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning0
Boosting Federated Learning Convergence with Prototype Regularization0
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning0
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers0
Achieving Personalized Federated Learning with Sparse Local Models0
Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles0
Dynamic Federated Learning0
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures0
Dynamic Fair Federated Learning Based on Reinforcement Learning0
Dynamic Differential-Privacy Preserving SGD0
Boosting Fairness and Robustness in Over-the-Air Federated Learning0
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application0
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection0
Dynamic Clustering in Federated Learning0
Dynamic backdoor attacks against federated learning0
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources0
An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation0
Dynamic Attention-based Communication-Efficient Federated Learning0
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning0
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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