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 451500 of 6771 papers

TitleStatusHype
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data SubspacesCode1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence GuaranteeCode1
Efficient On-device Training via Gradient FilteringCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health RecordsCode1
FedVSR: Towards Model-Agnostic Federated Learning in Video Super-ResolutionCode1
Eluding Secure Aggregation in Federated Learning via Model InconsistencyCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
A New Federated Learning Framework Against Gradient Inversion AttacksCode1
End-to-End Evaluation of Federated Learning and Split Learning for Internet of ThingsCode1
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation ModelsCode1
Few-Shot Model Agnostic Federated LearningCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare ApplicationsCode1
Energy-Latency Attacks via Sponge PoisoningCode1
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias ReductionCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
Proportional Fairness in Federated LearningCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
Evaluating Gradient Inversion Attacks and Defenses in Federated LearningCode1
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of ThingsCode1
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated LearningCode1
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated LearningCode1
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated LearningCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and PrivacyCode1
FLTracer: Accurate Poisoning Attack Provenance in Federated LearningCode1
Exploiting Label Skews in Federated Learning with Model ConcatenationCode1
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning SimulationsCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated LearningCode1
FACMIC: Federated Adaptative CLIP Model for Medical Image ClassificationCode1
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTCode1
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated DistillationCode1
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion AttacksCode1
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity LearningCode1
Generative Autoregressive Transformers for Model-Agnostic Federated MRI ReconstructionCode1
Addressing Algorithmic Disparity and Performance Inconsistency in Federated LearningCode1
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service DetectionCode1
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of ThingsCode1
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI ModalitiesCode1
Fast Federated Learning in the Presence of Arbitrary Device UnavailabilityCode1
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client VectorsCode1
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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