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 1501–1550 of 6771 papers

TitleStatusHype
Auction Based Clustered Federated Learning in Mobile Edge Computing System—0
Deep Unfolding-based Weighted Averaging for Federated Learning in Heterogeneous Environments—0
A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control—0
Data-Heterogeneous Hierarchical Federated Learning with Mobility—0
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning—0
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach—0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning—0
Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbation—0
Defending Against Poisoning Attacks in Federated Learning with Blockchain—0
Defending against Poisoning Backdoor Attacks on Federated Meta-learning—0
Defending against Reconstruction Attack in Vertical Federated Learning—0
BadSFL: Backdoor Attack against Scaffold Federated Learning—0
BadVFL: Backdoor Attacks in Vertical Federated Learning—0
Defending against the Label-flipping Attack in Federated Learning—0
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning—0
Defending Label Inference Attacks in Split Learning under Regression Setting—0
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning—0
Defense Against Gradient Leakage Attacks via Learning to Obscure Data—0
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS—0
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems—0
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning—0
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg—0
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory—0
Delay-Aware Hierarchical Federated Learning—0
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning—0
Delayed Random Partial Gradient Averaging for Federated Learning—0
Delay Minimization for Federated Learning Over Wireless Communication Networks—0
Delay Optimization of a Federated Learning-based UAV-aided IoT network—0
Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updates—0
Delay-Tolerant Local SGD for Efficient Distributed Training—0
Delving into the Adversarial Robustness of Federated Learning—0
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices—0
Democratising Knowledge Representation with BioCypher—0
Democratizing AI in Africa: FL for Low-Resource Edge Devices—0
Balancing Privacy Protection and Interpretability in Federated Learning—0
Demystifying Hyperparameter Optimization in Federated Learning—0
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks—0
Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning—0
Demystifying the Effects of Non-Independence in Federated Learning—0
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning—0
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things—0
Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent—0
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning—0
DepthFL: Depthwise Federated Learning for Heterogeneous Clients—0
DER Forecast using Privacy Preserving Federated Learning—0
DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection—0
Design and Analysis of Uplink and Downlink Communications for Federated Learning—0
Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM—0
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning—0
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks—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