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 351400 of 6771 papers

TitleStatusHype
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
Analyzing Federated Learning through an Adversarial LensCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Agnostic Federated LearningCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of ThingsCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias ReductionCode1
Bayesian Framework for Gradient LeakageCode1
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare ApplicationsCode1
FedBN: Federated Learning on Non-IID Features via Local Batch NormalizationCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated LearningCode1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated LearningCode1
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User HeterogeneityCode1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy GuaranteesCode1
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware SchedulerCode1
Adaptive Federated OptimizationCode1
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated LearningCode1
FedCV: A Federated Learning Framework for Diverse Computer Vision TasksCode1
FedDANE: A Federated Newton-Type MethodCode1
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and CorrectionCode1
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation LearningCode1
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency SpaceCode1
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental RegularizationCode1
FedBE: Making Bayesian Model Ensemble Applicable to Federated LearningCode1
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous DrivingCode1
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphsCode1
Federated Adaptation for Foundation Model-based RecommendationsCode1
Federated Bayesian Optimization via Thompson SamplingCode1
Federated Class-Incremental LearningCode1
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public TransportationCode1
An Efficient Approach for Cross-Silo Federated Learning to RankCode1
FedMood: Federated Learning on Mobile Health Data for Mood DetectionCode1
Federated Domain Generalization for Image Recognition via Cross-Client Style TransferCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
Federated Few-shot LearningCode1
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated LearningCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local IterationsCode1
Federated Graph Classification over Non-IID GraphsCode1
A Better Alternative to Error Feedback for Communication-Efficient Distributed LearningCode1
Federated Knowledge DistillationCode1
Federated Learning-based Vehicle Trajectory Prediction against CyberattacksCode1
Federated Learning Enables Big Data for Rare Cancer Boundary DetectionCode1
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
Federated Learning for ASR based on Wav2vec 2.0Code1
Federated Learning for Malware Detection in IoT DevicesCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
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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