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 301350 of 6771 papers

TitleStatusHype
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with AdapterCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth LandscapeCode1
GPFedRec: Graph-guided Personalization for Federated RecommendationCode1
XTab: Cross-table Pretraining for Tabular TransformersCode1
Flame: Simplifying Topology Extension in Federated LearningCode1
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise HeterogeneityCode1
Federated Ensemble-Directed Offline Reinforcement LearningCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based OptimizationCode1
Personalized Federated Learning under Mixture of DistributionsCode1
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential PrivacyCode1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare ApplicationsCode1
Learning to Transmit with Provable Guarantees in Wireless Federated LearningCode1
Federated Incremental Semantic SegmentationCode1
Asynchronous Federated Continual LearningCode1
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good TeacherCode1
SLPerf: a Unified Framework for Benchmarking Split LearningCode1
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic SystemsCode1
CoDeC: Communication-Efficient Decentralized Continual LearningCode1
Re-thinking Federated Active Learning based on Inter-class DiversityCode1
Make Landscape Flatter in Differentially Private Federated LearningCode1
PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientsCode1
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed ClassifierCode1
TARGET: Federated Class-Continual Learning via Exemplar-Free DistillationCode1
Multi-metrics adaptively identifies backdoors in Federated learningCode1
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated LearningCode1
Semi-Federated Learning for Collaborative Intelligence in Massive IoT NetworksCode1
Combating Exacerbated Heterogeneity for Robust Models in Federated LearningCode1
GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic ForgettingCode1
Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge ComputingCode1
Active Membership Inference Attack under Local Differential Privacy in Federated LearningCode1
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization AccuracyCode1
Federated Learning for ASR based on Wav2vec 2.0Code1
Multimodal Federated Learning via Contrastive Representation EnsembleCode1
Revisiting Weighted Aggregation in Federated Learning with Neural NetworksCode1
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization GuaranteesCode1
One-Shot Federated Conformal PredictionCode1
Byzantine-Robust Learning on Heterogeneous Data via Gradient SplittingCode1
XFL: A High Performace, Lightweighted Federated Learning FrameworkCode1
Federated Learning as Variational Inference: A Scalable Expectation Propagation ApproachCode1
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime DetectionCode1
No One Left Behind: Real-World Federated Class-Incremental LearningCode1
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution DetectionCode1
FedFA: Federated Feature AugmentationCode1
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
Federated Recommendation with Additive PersonalizationCode1
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological DataCode1
On the Vulnerability of Backdoor Defenses for Federated LearningCode1
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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