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 1–50 of 6771 papers

TitleStatusHype
Towards One-shot Federated Learning: Advances, Challenges, and Future DirectionsCode4
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated LearningCode4
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning SystemCode4
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical TrainingCode4
Differential Privacy: What is all the noise about?Code4
Eliminating Domain Bias for Federated Learning in Representation SpaceCode4
FLEX: FLEXible Federated Learning FrameworkCode4
FedCP: Separating Feature Information for Personalized Federated Learning via Conditional PolicyCode4
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and BenchmarkCode4
A Survey on LoRA of Large Language ModelsCode3
HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and BenchmarkCode3
Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised LearningCode3
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language ModelsCode3
pfl-research: simulation framework for accelerating research in Private Federated LearningCode3
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-MultinomialsCode3
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated LearningCode3
Global and Local Prompts Cooperation via Optimal Transport for Federated LearningCode2
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare SettingsCode2
Adaptive Personalized Federated LearningCode2
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
FedML: A Research Library and Benchmark for Federated Machine LearningCode2
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated LearningCode2
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated LearningCode2
FedModule: A Modular Federated Learning FrameworkCode2
Adaptive Latent-Space Constraints in Personalized FLCode2
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated LearningCode2
Federated Learning with New Knowledge: Fundamentals, Advances, and FuturesCode2
FedCLIP: Fast Generalization and Personalization for CLIP in Federated LearningCode2
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank AdaptationsCode2
fluke: Federated Learning Utility frameworK for Experimentation and researchCode2
FedFMS: Exploring Federated Foundation Models for Medical Image SegmentationCode2
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full ModelCode2
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language ModelsCode2
FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset DistillationCode2
FedGH: Heterogeneous Federated Learning with Generalized Global HeaderCode2
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing IntegrationCode2
DPAUC: Differentially Private AUC Computation in Federated LearningCode2
Advances and Open Problems in Federated LearningCode2
A Comprehensive Guide to Explainable AI: From Classical Models to LLMsCode2
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare ApplicationsCode2
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual LearningCode2
Fed3DGS: Scalable 3D Gaussian Splatting with Federated LearningCode2
Advances in APPFL: A Comprehensive and Extensible Federated Learning FrameworkCode2
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation NetworkCode2
Analytic Federated LearningCode2
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic ForecastingCode2
Efficient Federated Learning Tiny Language Models for Mobile Network Feature PredictionCode2
COALA: A Practical and Vision-Centric Federated Learning PlatformCode2
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated DataCode2
BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated LearningCode2
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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