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 2551–2600 of 6771 papers

TitleStatusHype
Balancing Similarity and Complementarity for Federated Learning—0
Algorithm Fairness in AI for Medicine and Healthcare—0
Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning—0
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks—0
Demystifying Hyperparameter Optimization in Federated Learning—0
Balancing Privacy Protection and Interpretability in Federated Learning—0
Declarative Privacy-Preserving Inference Queries—0
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning—0
Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)—0
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping—0
ABC-FL: Anomalous and Benign client Classification in Federated Learning—0
Optimal query complexity for private sequential learning against eavesdropping—0
Secure Distributed On-Device Learning Networks With Byzantine Adversaries—0
Democratizing AI in Africa: FL for Low-Resource Edge Devices—0
Democratising Knowledge Representation with BioCypher—0
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning—0
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices—0
Delving into the Adversarial Robustness of Federated Learning—0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation—0
A-LAQ: Adaptive Lazily Aggregated Quantized Gradient—0
Delay-Tolerant Local SGD for Efficient Distributed Training—0
Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updates—0
Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning—0
Delay Optimization of a Federated Learning-based UAV-aided IoT network—0
Delay Minimization for Federated Learning Over Wireless Communication Networks—0
Balancing Client Participation in Federated Learning Using AoI—0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning—0
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning—0
Delayed Random Partial Gradient Averaging for Federated Learning—0
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning—0
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures—0
Delay-Aware Hierarchical Federated Learning—0
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory—0
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning—0
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg—0
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning—0
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration—0
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems—0
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS—0
A Joint Gradient and Loss Based Clustered Federated Learning Design—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
Defense Against Gradient Leakage Attacks via Learning to Obscure Data—0
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning—0
Defending Label Inference Attacks in Split Learning under Regression Setting—0
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning—0
BaFFLe: Backdoor detection via Feedback-based Federated Learning—0
Defending against the Label-flipping Attack in Federated Learning—0
BadVFL: Backdoor Attacks in Vertical Federated Learning—0
Defending against Reconstruction Attack in Vertical Federated Learning—0
BadSFL: Backdoor Attack against Scaffold Federated Learning—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