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 2001–2050 of 6771 papers

TitleStatusHype
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout—0
Entity Resolution and Federated Learning get a Federated Resolution—0
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach—0
EPIC: Enhancing Privacy through Iterative Collaboration—0
Epidemic Decision-making System Based Federated Reinforcement Learning—0
Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning—0
Equitable Federated Learning with Activation Clustering—0
Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment—0
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination—0
Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience—0
Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression—0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning—0
Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent—0
ESMFL: Efficient and Secure Models for Federated Learning—0
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning—0
Byzantine-Robust Decentralized Federated Learning—0
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization—0
Efficient Privacy Preserving Edge Computing Framework for Image Classification—0
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning—0
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks—0
Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping—0
A distillation-based approach integrating continual learning and federated learning for pervasive services—0
A Coalition Formation Game Approach for Personalized Federated Learning—0
Evaluating Multi-Global Server Architecture for Federated Learning—0
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation—0
Evaluating the Communication Efficiency in Federated Learning Algorithms—0
Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction—0
Efficient Model Compression for Hierarchical Federated Learning—0
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization—0
Byzantine-Robust and Privacy-Preserving Framework for FedML—0
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning—0
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System—0
Efficient Language Model Architectures for Differentially Private Federated Learning—0
Event-Triggered Decentralized Federated Learning over Resource-Constrained Edge Devices—0
Evidential Federated Learning for Skin Lesion Image Classification—0
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning—0
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment—0
Efficient Image Representation Learning with Federated Sampled Softmax—0
Efficient Fully Distributed Federated Learning with Adaptive Local Links—0
Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning—0
A New Implementation of Federated Learning for Privacy and Security Enhancement—0
ExclaveFL: Providing Transparency to Federated Learning using Exclaves—0
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation—0
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression—0
Byzantine-Resilient Secure Federated Learning—0
Efficient Federated Split Learning for Large Language Models over Communication Networks—0
Efficient Federated Learning with Timely Update Dissemination—0
Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises—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