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 44014450 of 6771 papers

TitleStatusHype
FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation0
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks0
FedSampling: A Better Sampling Strategy for Federated Learning0
FedSat: A Statistical Aggregation Approach for Class Imbalanced Clients in Federated Learning0
FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation0
FedScalar: A Communication efficient Federated Learning0
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data0
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning0
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning0
FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning0
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning0
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection0
FedSiam: Towards Adaptive Federated Semi-Supervised Learning0
FedSheafHN: Personalized Federated Learning on Graph-structured Data0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation0
FedShuffle: Recipes for Better Use of Local Work in Federated Learning0
FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification0
Fed-Sim: Federated Simulation for Medical Imaging0
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation0
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching0
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification0
FedSmart: An Auto Updating Federated Learning Optimization Mechanism0
FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks0
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating0
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm0
FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature0
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations0
FedSpaLLM: Federated Pruning of Large Language Models0
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning0
FedSpectral+: Spectral Clustering using Federated Learning0
FedSpeech: Federated Text-to-Speech with Continual Learning0
FedSplit: An algorithmic framework for fast federated optimization0
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation0
FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients0
FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT System0
FedSSC: Shared Supervised-Contrastive Federated Learning0
FedSS: Federated Learning with Smart Selection of clients0
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm0
FedStack: Personalized activity monitoring using stacked federated learning0
FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning0
FedST: Secure Federated Shapelet Transformation for Time Series Classification0
FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems0
FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework0
FedSV: Byzantine-Robust Federated Learning via Shapley Value0
FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA0
FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for 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