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 41514200 of 6771 papers

TitleStatusHype
Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles0
Federated Graph Representation Learning using Self-Supervision0
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares SolutionCode0
Addressing Heterogeneity in Federated Learning via Distributional TransformationCode0
Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means ClusteringCode0
FeDXL: Provable Federated Learning for Deep X-Risk OptimizationCode0
Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks0
Personalized Federated Learning via Heterogeneous Modular NetworksCode0
UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES0
FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings0
Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agents0
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model CommunicationCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
Detection and Prevention Against Poisoning Attacks in Federated Learning0
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise0
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression0
NVIDIA FLARE: Federated Learning from Simulation to Real-WorldCode2
Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networks0
Federated Learning and Meta Learning: Approaches, Applications, and Directions0
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated LearningCode1
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning0
On-Demand Sampling: Learning Optimally from Multiple DistributionsCode0
Towards Quantum-Enabled 6G Slicing0
Federated Learning via Unmanned Aerial Vehicle0
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario0
An Improved Algorithm for Clustered Federated LearningCode0
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information0
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated LearningCode1
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis0
Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning0
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks0
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences0
TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing0
Random Orthogonalization for Federated Learning in Massive MIMO Systems0
FedForgery: Generalized Face Forgery Detection with Residual Federated LearningCode1
Industry-Scale Orchestrated Federated Learning for Drug Discovery0
Data Subsampling for Bayesian Neural NetworksCode0
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated LearningCode0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation0
FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation0
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability0
A Primal-Dual Algorithm for Hybrid Federated Learning0
FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning0
CrowdGuard: Federated Backdoor Detection in Federated LearningCode0
Where to Begin? On the Impact of Pre-Training and Initialization in Federated LearningCode1
Toward Secure and Private Over-the-Air Federated Learning0
ScionFL: Efficient and Robust Secure Quantized Aggregation0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning0
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated LearningCode0
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation0
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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