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 150 of 6771 papers

TitleStatusHype
Towards One-shot Federated Learning: Advances, Challenges, and Future DirectionsCode4
Eliminating Domain Bias for Federated Learning in Representation SpaceCode4
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical TrainingCode4
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning SystemCode4
Differential Privacy: What is all the noise about?Code4
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and BenchmarkCode4
FLEX: FLEXible Federated Learning FrameworkCode4
FedCP: Separating Feature Information for Personalized Federated Learning via Conditional PolicyCode4
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated LearningCode4
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
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated LearningCode2
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
FedGraph: A Research Library and Benchmark for Federated Graph LearningCode2
FedML: A Research Library and Benchmark for Federated Machine LearningCode2
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated LearningCode2
Analytic Federated LearningCode2
FedModule: A Modular Federated Learning FrameworkCode2
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated LearningCode2
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic ForecastingCode2
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
Federated Learning with New Knowledge: Fundamentals, Advances, and FuturesCode2
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full ModelCode2
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare ApplicationsCode2
FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset DistillationCode2
FedFMS: Exploring Federated Foundation Models for Medical Image SegmentationCode2
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation NetworkCode2
DPAUC: Differentially Private AUC Computation in Federated LearningCode2
Adaptive Latent-Space Constraints in Personalized FLCode2
Adaptive Personalized Federated LearningCode2
Advances and Open Problems in Federated LearningCode2
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language ModelsCode2
Fed3DGS: Scalable 3D Gaussian Splatting with Federated LearningCode2
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual LearningCode2
Advances in APPFL: A Comprehensive and Extensible Federated Learning FrameworkCode2
Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing IntegrationCode2
Confidential Federated ComputationsCode2
Efficient Federated Learning Tiny Language Models for Mobile Network Feature PredictionCode2
Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated LearningCode2
COALA: A Practical and Vision-Centric Federated Learning PlatformCode2
A Survey on Federated Fine-tuning of Large Language ModelsCode2
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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