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 12511300 of 6771 papers

TitleStatusHype
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
Controlling Participation in Federated Learning with FeedbackCode0
Federated Learning with Intermediate Representation RegularizationCode0
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning FrameworkCode0
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight AggregationCode0
Federated Learning with a Single Shared ImageCode0
Federated Learning via Plurality VoteCode0
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated LearningCode0
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical DiagnosisCode0
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and AnalysisCode0
Accelerating Federated Learning with a Global Biased OptimiserCode0
Federated Learning under Partially Class-Disjoint Data via Manifold ReshapingCode0
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on ConvergenceCode0
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer NetworksCode0
A Field Guide to Federated OptimizationCode0
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and ProtectionCode0
Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home EnvironmentsCode0
Federated Learning of Medical Concepts Embedding using BEHRTCode0
Federated Learning of Large Models at the Edge via Principal Sub-Model TrainingCode0
Federated Learning of Models Pre-Trained on Different Features with Consensus GraphsCode0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
Federated Learning Meets Fairness and Differential PrivacyCode0
Federated Causal Inference from Observational DataCode0
Federated Learning with Additional Mechanisms on Clients to Reduce Communication CostsCode0
Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome TreatmentCode0
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data SourcesCode0
Federated Learning in ASR: Not as Easy as You ThinkCode0
Congruent Learning for Self-Regulated Federated Learning in 6GCode0
Anchor Sampling for Federated Learning with Partial Client ParticipationCode0
Federated Learning Hyper-Parameter Tuning from a System PerspectiveCode0
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic DataCode0
A Federated Random Forest Solution for Secure Distributed Machine LearningCode0
A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future ResearchCode0
Federated Learning From Big Data Over NetworksCode0
Federated Learning in Unreliable and Resource-Constrained Cellular Wireless NetworksCode0
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object DetectionCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture ModelsCode0
Federated Learning for Mobile Keyboard PredictionCode0
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism DesignCode0
Federated Learning for Privacy-Preserving Feedforward Control in Multi-Agent SystemsCode0
Federated LoRA with Sparse CommunicationCode0
Federated Learning for Data StreamsCode0
Federated Learning for Keyword SpottingCode0
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New AlgorithmsCode0
Federated learning compression designed for lightweight communicationsCode0
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated LearningCode0
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster SamplingCode0
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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