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 64016450 of 6771 papers

TitleStatusHype
Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise0
Prioritized Multi-Criteria Federated Learning0
HyperTune: Dynamic Hyperparameter Tuning For Efficient Distribution of DNN Training Over Heterogeneous Systems0
Less is More: A privacy-respecting Android malware classifier using Federated LearningCode0
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G0
FetchSGD: Communication-Efficient Federated Learning with Sketching0
FedBoosting: Federated Learning with Gradient Protected Boosting for Text RecognitionCode0
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning0
Privacy Amplification via Random Check-Ins0
Model Fusion with Kullback--Leibler DivergenceCode0
Data-driven geophysics: from dictionary learning to deep learning0
Quality Inference in Federated Learning with Secure Aggregation0
VAFL: a Method of Vertical Asynchronous Federated Learning0
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg0
Data science and AI in FinTech: An overview0
Federated Learning of User Authentication Models0
Client Adaptation improves Federated Learning with Simulated Non-IID ClientsCode0
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning0
Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data0
Backdoor attacks and defenses in feature-partitioned collaborative learning0
A Distributed Cubic-Regularized Newton Method for Smooth Convex Optimization over Networks0
A Federated F-score Based Ensemble Model for Automatic Rule Extraction0
Personalized Cross-Silo Federated Learning on Non-IID Data0
Self-organizing Democratized Learning: Towards Large-scale Distributed Learning SystemsCode0
Coded Computing for Federated Learning at the Edge0
Sharing Models or Coresets: A Study based on Membership Inference Attack0
Experiments of Federated Learning for COVID-19 Chest X-ray Images0
Delay Minimization for Federated Learning Over Wireless Communication Networks0
Privacy Threats Against Federated Matrix Factorization0
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning0
On the Outsized Importance of Learning Rates in Local Update MethodsCode0
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy0
Federated Learning with Compression: Unified Analysis and Sharp Guarantees0
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications0
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guaranteesCode0
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms0
Security and Privacy Preserving Deep Learning0
Exact Support Recovery in Federated Regression with One-shot Communication0
Byzantine-Resilient High-Dimensional Federated Learning0
D2P-Fed: Differentially Private Federated Learning With Efficient Communication0
Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC0
FedFMC: Sequential Efficient Federated Learning on Non-iid Data0
DEED: A General Quantization Scheme for Communication Efficiency in Bits0
Federated Learning With Quantized Global Model Updates0
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup0
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets0
Federated Survival Analysis with Discrete-Time Cox Models0
Robust Federated Learning: The Case of Affine Distribution Shifts0
Fusion Learning: A One Shot Federated Learning0
Ensemble Distillation for Robust Model Fusion in Federated LearningCode0
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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