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 60016050 of 6771 papers

TitleStatusHype
FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsCode0
FedRTS: Federated Robust Pruning via Combinatorial Thompson SamplingCode0
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine LearningCode0
LightTR: A Lightweight Framework for Federated Trajectory RecoveryCode0
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated LearningCode0
FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated LearningCode0
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated LearningCode0
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space ReconstructionCode0
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight AggregationCode0
Differentially private federated deep learning for multi-site medical image segmentationCode0
User-Level Label Leakage from Gradients in Federated LearningCode0
Partitioned Variational Inference: A Framework for Probabilistic Federated LearningCode0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
Federated Representation Learning in the Under-Parameterized RegimeCode0
FedSC: Federated Learning with Semantic-Aware CollaborationCode0
FedScore: A privacy-preserving framework for federated scoring system developmentCode0
Scaling Federated Learning Solutions with Kubernetes for Synthesizing Histopathology ImagesCode0
Linear Speedup in Personalized Collaborative LearningCode0
Differentially Private Decentralized Learning with Random WalksCode0
The FeatureCloud AI Store for Federated Learning in Biomedicine and BeyondCode0
A Federated Framework for LLM-based RecommendationCode0
LLM-QFL: Distilling Large Language Model for Quantum Federated LearningCode0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
PathFL: Multi-Alignment Federated Learning for Pathology Image SegmentationCode0
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language ModelCode0
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
Anomaly Localization in Model Gradients Under Backdoor Attacks Against Federated LearningCode0
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated LearningCode0
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDCode0
Scaling Probabilistic Circuits via Data PartitioningCode0
Adaptive Expert Models for Personalization in Federated LearningCode0
FedSiKD: Clients Similarity and Knowledge Distillation: Addressing Non-i.i.d. and Constraints in Federated LearningCode0
BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search enginesCode0
Federated Prediction-Powered Inference from Decentralized DataCode0
Federated Over-Air Subspace Tracking from Incomplete and Corrupted DataCode0
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge CollaborationCode0
PeerFL: A Simulator for Peer-to-Peer Federated Learning at ScaleCode0
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients UpdateCode0
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial LearningCode0
FedSkip: Combatting Statistical Heterogeneity with Federated Skip AggregationCode0
FedSlate:A Federated Deep Reinforcement Learning Recommender SystemCode0
Fairness and Privacy in Federated Learning and Their Implications in HealthcareCode0
FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural NetworksCode0
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex LossesCode0
Semi-Supervised Federated Peer Learning for Skin Lesion ClassificationCode0
FedSODA: Federated Cross-assessment and Dynamic Aggregation for Histopathology SegmentationCode0
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome PredictionCode0
Federated Optimization for Heterogeneous NetworksCode0
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical DiagnosisCode0
Federated Noisy Client LearningCode0
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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