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 50015050 of 6771 papers

TitleStatusHype
Optimization with Access to Auxiliary InformationCode0
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images0
FedHarmony: Unlearning Scanner Bias with Distributed DataCode0
Asynchronous Hierarchical Federated Learning0
VFed-SSD: Towards Practical Vertical Federated Advertising0
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting0
Near-Optimal Collaborative Learning in BanditsCode0
Secure Federated Clustering0
Confederated Learning: Federated Learning with Decentralized Edge Servers0
CalFAT: Calibrated Federated Adversarial Training with Label SkewnessCode0
FedAUXfdp: Differentially Private One-Shot Federated Distillation0
FRAug: Tackling Federated Learning with Non-IID Features via Representation AugmentationCode0
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
Maximizing Global Model Appeal in Federated Learning0
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning0
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight AggregationCode0
Federated Semi-Supervised Learning with Prototypical NetworksCode0
FedControl: When Control Theory Meets Federated Learning0
FadMan: Federated Anomaly Detection across Multiple Attributed Networks0
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge0
Combating Client Dropout in Federated Learning via Friend Model Substitution0
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning0
Mixed Federated Learning: Joint Decentralized and Centralized Learning0
Federated Split BERT for Heterogeneous Text Classification0
Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information0
Cali3F: Calibrated Fast Fair Federated Recommendation System0
QUIC-FL: Quick Unbiased Compression for Federated Learning0
A Fair Federated Learning Framework With Reinforcement Learning0
A Unified Analysis of Federated Learning with Arbitrary Client Participation0
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking0
Federated Adaptation of Reservoirs via Intrinsic Plasticity0
Federated Self-supervised Learning for Heterogeneous Clients0
VeriFi: Towards Verifiable Federated Unlearning0
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy JudgmentCode0
Federated singular value decomposition for high dimensional dataCode0
Differentially Private AUC Computation in Vertical Federated Learning0
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning0
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning0
Personalized Federated Learning with Server-Side InformationCode0
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing0
FL Games: A federated learning framework for distribution shifts0
CELEST: Federated Learning for Globally Coordinated Threat Detection0
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation0
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation0
Federated Distillation based Indoor Localization for IoT Networks0
Fed-DART and FACT: A solution for Federated Learning in a production environment0
Semi-Decentralized Federated Learning with Collaborative Relaying0
Incentivizing 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