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 201250 of 6771 papers

TitleStatusHype
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
Differentially Private Vertical Federated ClusteringCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Distantly Supervised Relation Extraction in Federated SettingsCode1
A Blockchain-based Decentralized Federated Learning Framework with Committee ConsensusCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
DYNAFED: Tackling Client Data Heterogeneity with Global DynamicsCode1
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated LearningCode1
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth LandscapeCode1
EasyFL: A Low-code Federated Learning Platform For DummiesCode1
FedGiA: An Efficient Hybrid Algorithm for Federated LearningCode1
TARGET: Federated Class-Continual Learning via Exemplar-Free DistillationCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
Efficient On-device Training via Gradient FilteringCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Active Membership Inference Attack under Local Differential Privacy in Federated LearningCode1
End-to-End Evaluation of Federated Learning and Split Learning for Internet of ThingsCode1
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
Energy-Latency Attacks via Sponge PoisoningCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of ThingsCode1
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation ModelsCode1
Non-IID Quantum Federated Learning with One-shot Communication ComplexityCode1
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence GuaranteeCode1
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated LearningCode1
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTCode1
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated DistillationCode1
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
Fast Federated Learning by Balancing Communication Trade-OffsCode1
Fast Federated Learning in the Presence of Arbitrary Device UnavailabilityCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
Feature Inference Attack on Model Predictions in Vertical Federated LearningCode1
A Distributed Trust Framework for Privacy-Preserving Machine LearningCode1
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias ReductionCode1
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated LearningCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare ApplicationsCode1
FedBN: Federated Learning on Non-IID Features via Local Batch NormalizationCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Asynchronous Federated Continual LearningCode1
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User HeterogeneityCode1
FedCM: Federated Learning with Client-level MomentumCode1
ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRICode1
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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