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 51015150 of 6771 papers

TitleStatusHype
Certified Federated Adversarial Training0
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical ImagesCode1
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity0
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction0
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning BetterCode1
Cross-Domain Federated Learning in Medical Imaging0
Federated Learning with Superquantile Aggregation for Heterogeneous DataCode1
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems0
CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning0
Quality monitoring of federated Covid-19 lesion segmentation0
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Communication-Efficient Distributed SGD with Compressed Sensing0
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method0
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization0
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning0
Federated Learning for Face Recognition with Gradient Correction0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
Scatterbrained: A flexible and expandable pattern for decentralized machine learningCode1
Optimal Rate Adaption in Federated Learning with Compressed Communications0
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors0
Efficient and Reliable Overlay Networks for Decentralized Federated Learning0
Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image AugmentationCode0
Communication-Efficient Federated Learning for Neural Machine Translation0
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with SparsificationCode0
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning0
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating0
Efficient Device Scheduling with Multi-Job Federated Learning0
Sequence-level self-learning with multiple hypotheses0
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical recordsCode0
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning0
Federated Two-stage Learning with Sign-based Voting0
Specificity-Preserving Federated Learning for MR Image ReconstructionCode1
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost0
Location Leakage in Federated Signal Maps0
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless NetworksCode1
When the Curious Abandon Honesty: Federated Learning Is Not Private0
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding0
Intrinisic Gradient Compression for Federated Learning0
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks0
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning0
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks0
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning0
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving0
FedRAD: Federated Robust Adaptive Distillation0
Context-Aware Online Client Selection for Hierarchical Federated Learning0
Projected Federated Averaging with Heterogeneous Differential PrivacyCode1
IndicFed: A Federated Approach for Sentiment Analysis in Indic Languages0
Models of fairness in federated learning0
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data0
Improving Differentially Private SGD via Randomly Sparsified GradientsCode0
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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