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 101150 of 6771 papers

TitleStatusHype
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge DevicesCode1
One-shot Federated Learning via Synthetic Distiller-Distillate CommunicationCode1
Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model ArchitecturesCode1
FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity DataCode1
Identify Backdoored Model in Federated Learning via Individual UnlearningCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
Personalized Federated Learning via Feature Distribution AdaptationCode1
Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated LearningCode1
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model FusionCode1
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept DriftCode1
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked DataCode1
FedMSE: Federated learning for IoT network intrusion detectionCode1
Why Go Full? Elevating Federated Learning Through Partial Network UpdatesCode1
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language ModelsCode1
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation ModelsCode1
FACMIC: Federated Adaptative CLIP Model for Medical Image ClassificationCode1
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and MethodCode1
Federated Learning with Label-Masking DistillationCode1
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving MLCode1
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and PurificationCode1
Tackling Data Heterogeneity in Federated Learning via Loss DecompositionCode1
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in ColonoscopyCode1
Tackling Noisy Clients in Federated Learning with End-to-end Label CorrectionCode1
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel ExtractionCode1
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing ModalitiesCode1
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical ImagingCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
FedMLP: Federated Multi-Label Medical Image Classification under Task HeterogeneityCode1
Personalized Federated Continual Learning via Multi-granularity PromptCode1
Safely Learning with Private Data: A Federated Learning Framework for Large Language ModelCode1
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI ModalitiesCode1
Save It All: Enabling Full Parameter Tuning for Federated Large Language Models via Cycle Block Gradient DescentCode1
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly DetectionCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
Redefining Contributions: Shapley-Driven Federated LearningCode1
Pursuing Overall Welfare in Federated Learning through Sequential Decision MakingCode1
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware MinimizationCode1
Adaptive and Parallel Split Federated Learning in Vehicular Edge ComputingCode1
Federated Learning with Bilateral Curation for Partially Class-Disjoint DataCode1
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew ResilienceCode1
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
Federated Unsupervised Domain Generalization using Global and Local Alignment of GradientsCode1
Vertical Federated Learning for Effectiveness, Security, Applicability: A SurveyCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Thinking Forward: Memory-Efficient Federated Finetuning of Language ModelsCode1
DAGER: Exact Gradient Inversion for Large Language ModelsCode1
Unlearning during Learning: An Efficient Federated Machine Unlearning MethodCode1
Overcoming the Challenges of Batch Normalization in Federated LearningCode1
Ferrari: Federated Feature Unlearning via Optimizing Feature SensitivityCode1
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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