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 44514500 of 6771 papers

TitleStatusHype
FLAGS Framework for Comparative Analysis of Federated Learning Algorithms0
Robust Split Federated Learning for U-shaped Medical Image NetworksCode0
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO0
AFLGuard: Byzantine-robust Asynchronous Federated Learning0
Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning0
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals0
GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation TechniquesCode0
Federated Learning for Inference at Anytime and Anywhere0
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential PrivacyCode0
Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning0
Reconstructing Training Data from Model Gradient, Provably0
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles0
PaDPaF: Partial Disentanglement with Partially-Federated GANsCode0
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning0
FedCC: Robust Federated Learning against Model Poisoning Attacks0
Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agents0
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks0
Encrypted machine learning of molecular quantum propertiesCode0
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics0
Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning0
Distributed Pruning Towards Tiny Neural Networks in Federated Learning0
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation0
Security Analysis of SplitFed Learning0
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding0
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning0
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework0
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning0
Self-supervised On-device Federated Learning from Unlabeled Streams0
Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning0
Faster Adaptive Federated Learning0
PGFed: Personalize Each Client's Global Objective for Federated LearningCode0
Towards Practical Few-shot Federated NLP0
Vertical Federated Learning: A Structured Literature Review0
PiPar: Pipeline Parallelism for Collaborative Machine Learning0
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems0
Early prediction of the risk of ICU mortality with Deep Federated LearningCode0
Hijack Vertical Federated Learning Models As One Party0
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning0
Privacy-Preserving Federated Deep Clustering based on GAN0
On the Design of Communication-Efficient Federated Learning for Health Monitoring0
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning0
Scalable Hierarchical Over-the-Air Federated Learning0
Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview0
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise0
Flow: Per-Instance Personalized Federated Learning Through Dynamic RoutingCode0
AquaFeL-PSO: A Monitoring System for Water Resources using Autonomous Surface Vehicles based on Multimodal PSO and Federated Learning0
Federated Learning Attacks and Defenses: A Survey0
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning0
MDA: Availability-Aware Federated Learning Client SelectionCode0
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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