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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3451–3500 of 6771 papers

TitleStatusHype
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks—0
Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space—0
Review of Mathematical Optimization in Federated Learning—0
Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates—0
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels—0
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis—0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks—0
Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions—0
Revocable Federated Learning: A Benchmark of Federated Forest—0
Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning—0
Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review—0
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices—0
Reward-Based 1-bit Compressed Federated Distillation on Blockchain—0
Rewarding High-Quality Data via Influence Functions—0
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning—0
RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning—0
Riemannian Federated Learning via Averaging Gradient Stream—0
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation—0
RIFLES: Resource-effIcient Federated LEarning via Scheduling—0
Right Reward Right Time for Federated Learning—0
RingFed: Reducing Communication Costs in Federated Learning on Non-IID Data—0
RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility—0
Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients—0
RLSA-PFL: Robust Lightweight Secure Aggregation with Model Inconsistency Detection in Privacy-Preserving Federated Learning—0
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties—0
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach—0
Robust Convergence in Federated Learning through Label-wise Clustering—0
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning—0
Robust Decentralized Learning with Local Updates and Gradient Tracking—0
RobustFed: A Truth Inference Approach for Robust Federated Learning—0
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA—0
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition—0
Robust Federated Learning against Model Perturbation in Edge Networks—0
Robust Federated Learning for execution time-based device model identification under label-flipping attack—0
Robust Federated Learning for Neural Networks—0
Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation—0
Robust Federated Learning in a Heterogeneous Environment—0
Robust Federated Learning in the Face of Covariate Shift: A Magnitude Pruning with Hybrid Regularization Framework for Enhanced Model Aggregation—0
Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach—0
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks—0
Robust Federated Learning on Edge Devices with Domain Heterogeneity—0
Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping—0
Robust Federated Learning: The Case of Affine Distribution Shifts—0
Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters—0
Robust Federated Learning via Over-The-Air Computation—0
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data—0
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying—0
Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management—0
Robust Federated Learning with Noisy Communication—0
Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified