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 46514700 of 6771 papers

TitleStatusHype
A Unified Analysis of Federated Learning with Arbitrary Client Participation0
Cali3F: Calibrated Fast Fair Federated Recommendation System0
Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information0
Federated Split BERT for Heterogeneous Text Classification0
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning0
A Fair Federated Learning Framework With Reinforcement Learning0
QUIC-FL: Quick Unbiased Compression for Federated Learning0
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias ReductionCode1
Combating Client Dropout in Federated Learning via Friend Model Substitution0
Federated Adaptation of Reservoirs via Intrinsic Plasticity0
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking0
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision TransformersCode1
VeriFi: Towards Verifiable Federated Unlearning0
Federated Self-supervised Learning for Heterogeneous Clients0
Differentially Private AUC Computation in Vertical Federated Learning0
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy JudgmentCode0
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy GuaranteesCode1
Towards a Defense Against Federated Backdoor Attacks Under Continuous TrainingCode1
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning0
Federated singular value decomposition for high dimensional dataCode0
Optimizing Performance of Federated Person Re-identification: Benchmarking and AnalysisCode1
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning0
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing0
Semi-Decentralized Federated Learning with Collaborative Relaying0
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation0
Personalized Federated Learning with Server-Side InformationCode0
FL Games: A federated learning framework for distribution shifts0
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation0
Orchestra: Unsupervised Federated Learning via Globally Consistent ClusteringCode1
CELEST: Federated Learning for Globally Coordinated Threat Detection0
Federated Distillation based Indoor Localization for IoT Networks0
Fed-DART and FACT: A solution for Federated Learning in a production environment0
Federated Learning Aggregation: New Robust Algorithms with Guarantees0
Test-Time Robust Personalization for Federated LearningCode1
Incentivizing Federated Learning0
Robust Quantity-Aware Aggregation for Federated Learning0
Aligning Logits Generatively for Principled Black-Box Knowledge DistillationCode0
E2FL: Equal and Equitable Federated Learning0
Kernel Normalized Convolutional NetworksCode0
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels0
FedAdapter: Efficient Federated Learning for Modern NLPCode1
On the Decentralization of Blockchain-enabled Asynchronous Federated Learning0
Service Delay Minimization for Federated Learning over Mobile Devices0
Differential Privacy: What is all the noise about?Code4
FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID DataCode1
Federated learning: Applications, challenges and future directions0
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings0
Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting0
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles0
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical ImagingCode1
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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