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 24512500 of 6771 papers

TitleStatusHype
Federated Hyperdimensional Computing0
Federated cINN Clustering for Accurate Clustered Federated Learning0
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination0
Federated Class-Incremental Learning with Prompting0
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning0
Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests0
Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions0
Federated Matrix Factorization: Algorithm Design and Application to Data Clustering0
Compression Boosts Differentially Private Federated Learning0
Compression with Exact Error Distribution for Federated Learning0
Federated Combinatorial Multi-Agent Multi-Armed Bandits0
Federated Neural Graph Databases0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients0
Federated Composite Saddle Point Optimization0
Federated Compositional Deep AUC Maximization0
Federated Computing -- Survey on Building Blocks, Extensions and Systems0
Federated Conditional Stochastic Optimization0
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression0
Federated Continual 3D Segmentation With Single-round Communication0
Federated Continual Instruction Tuning0
Federated Continual Learning: Concepts, Challenges, and Solutions0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning0
Federated Continual Learning through distillation in pervasive computing0
A Federated Learning Approach to Privacy Preserving Offensive Language Identification0
FedDICE: A ransomware spread detection in a distributed integrated clinical environment using federated learning and SDN based mitigation0
Federated Continual Novel Class Learning0
Federated Contrastive Learning for Decentralized Unlabeled Medical Images0
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning0
Federated Contrastive Learning for Volumetric Medical Image Segmentation0
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading0
Federated Contrastive Learning of Graph-Level Representations0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching0
Federated Covariate Shift Adaptation for Missing Target Output Values0
FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems0
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy0
Federated CycleGAN for Privacy-Preserving Image-to-Image Translation0
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases0
Federated Data-Driven Kalman Filtering for State Estimation0
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning0
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization0
Confidence-based federated distillation for vision-based lane-centering0
Privacy-Preserving Federated Deep Clustering based on GAN0
Active Learning Solution on Distributed Edge Computing0
Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO0
Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy0
Federated Deep Learning with Bayesian Privacy0
Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data0
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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