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 51100 of 6771 papers

TitleStatusHype
Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and AggregationCode2
FedGH: Heterogeneous Federated Learning with Generalized Global HeaderCode2
FedCLIP: Fast Generalization and Personalization for CLIP in Federated LearningCode2
NVIDIA FLARE: Federated Learning from Simulation to Real-WorldCode2
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare SettingsCode2
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving DesensitizationCode2
DPAUC: Differentially Private AUC Computation in Federated LearningCode2
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized HealthcareCode2
Secure & Private Federated NeuroimagingCode2
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated LearningCode2
FedML: A Research Library and Benchmark for Federated Machine LearningCode2
Adaptive Personalized Federated LearningCode2
Advances and Open Problems in Federated LearningCode2
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
ByzFL: Research Framework for Robust Federated LearningCode1
The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated LearningCode1
Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated LearningCode1
FNBench: Benchmarking Robust Federated Learning against Noisy LabelsCode1
Private Federated Learning using Preference-Optimized Synthetic DataCode1
mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixupCode1
FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt LearningCode1
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client VectorsCode1
UltraFlwr -- An Efficient Federated Medical and Surgical Object Detection FrameworkCode1
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service DetectionCode1
Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-MismatchCode1
FedVSR: Towards Model-Agnostic Federated Learning in Video Super-ResolutionCode1
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion AttacksCode1
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
Detecting Backdoor Attacks in Federated Learning via Direction Alignment InspectionCode1
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated LearningCode1
Secure On-Device Video OOD Detection Without BackpropagationCode1
FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User DataCode1
Subgraph Federated Learning for Local GeneralizationCode1
Federated nnU-Net for Privacy-Preserving Medical Image SegmentationCode1
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated ClientsCode1
Can Textual Gradient Work in Federated Learning?Code1
Mitigating Unintended Memorization with LoRA in Federated Learning for LLMsCode1
Generative Autoregressive Transformers for Model-Agnostic Federated MRI ReconstructionCode1
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated LearningCode1
Physics-Inspired Distributed Radio Map EstimationCode1
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian SamplingCode1
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital CollaborationCode1
Split Federated Learning Empowered Vehicular Edge Intelligence: Concept, Adaptive Design, and Future DirectionsCode1
Federated Unlearning with Gradient Descent and Conflict MitigationCode1
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated LearningCode1
SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated LearningCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
Optimizing Personalized Federated Learning through Adaptive Layer-Wise LearningCode1
A New Federated Learning Framework Against Gradient Inversion AttacksCode1
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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