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 43514400 of 6771 papers

TitleStatusHype
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference0
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning0
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning0
PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning0
When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMA0
Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer0
FedExP: Speeding Up Federated Averaging via ExtrapolationCode0
BayBFed: Bayesian Backdoor Defense for Federated Learning0
Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression0
Backdoor Attacks in Peer-to-Peer Federated Learning0
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-DistortionCode0
Accelerating Fair Federated Learning: Adaptive Federated Adam0
Energy Prediction using Federated LearningCode0
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation0
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret0
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?0
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges0
Federated Automatic Differentiation0
Label Inference Attack against Split Learning under Regression SettingCode0
Robust Knowledge Adaptation for Federated Unsupervised Person ReID0
SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes0
FedCliP: Federated Learning with Client Pruning0
From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray ClassificationCode0
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association0
Poisoning Attacks and Defenses in Federated Learning: A Survey0
FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano DevicesCode0
FedSSC: Shared Supervised-Contrastive Federated Learning0
Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks0
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application0
Federated Learning and Blockchain-enabled Fog-IoT Platform for Wearables in Predictive Healthcare0
Network Adaptive Federated Learning: Congestion and Lossy Compression0
Federated Learning for Energy Constrained IoT devices: A systematic mapping study0
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation0
FedDebug: Systematic Debugging for Federated Learning ApplicationsCode0
Why Batch Normalization Damage Federated Learning on Non-IID Data?Code0
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices0
Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problems0
IronForge: An Open, Secure, Fair, Decentralized Federated LearningCode0
Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks0
Single-round Self-supervised Distributed Learning using Vision Transformer0
Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems0
Federated Learning for Data StreamsCode0
Recent Advances on Federated Learning: A Systematic Survey0
Machine Learning for Large-Scale Optimization in 6G Wireless Networks0
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics0
Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation0
FedPD: Federated Open Set Recognition with Parameter Disentanglement0
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning FrameworkCode0
Personalized Semantics Excitation for Federated Image Classification0
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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