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 60516100 of 6771 papers

TitleStatusHype
Efficient Client Contribution Evaluation for Horizontal Federated Learning0
Blockchained Federated Learning for Threat Defense0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning0
Distributed Online Learning with Multiple Kernels0
Distributionally Robust Federated Averaging0
Federated Multi-armed Bandits with PersonalizationCode0
A Quantitative Metric for Privacy Leakage in Federated Learning0
QuPeL: Quantized Personalization with Applications to Federated Learning0
Federated Learning for Physical Layer Design0
Multiple Kernel-Based Online Federated Learning0
Clustering Algorithm to Detect Adversaries in Federated Learning0
Federated f-Differential PrivacyCode0
Sustainable Federated Learning0
When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System0
Personalized Federated Learning: A Unified Framework and Universal Optimization TechniquesCode0
Robust and Differentially Private Mean EstimationCode0
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning0
DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection0
Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach0
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications0
Scaling Neuroscience Research using Federated Learning0
On the Impact of Device and Behavioral Heterogeneity in Federated Learning0
A first look into the carbon footprint of federated learning0
MARINA: Faster Non-Convex Distributed Learning with CompressionCode0
Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems0
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients0
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients0
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure AggregationCode0
Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data0
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice0
Meta Federated Learning0
FLOP: Federated Learning on Medical Datasets using Partial Networks0
Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication ComplexityCode0
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation0
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication0
Security and Privacy for Artificial Intelligence: Opportunities and Challenges0
Coordinating Momenta for Cross-silo Federated Learning0
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning0
Multi-Tier Federated Learning for Vertically Partitioned Data0
DEAL: Decremental Energy-Aware Learning in a Federated System0
Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated LearningCode0
Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles0
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning0
Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings0
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation0
A Bayesian Federated Learning Framework with Online Laplace Approximation0
Provably Secure Federated Learning against Malicious Clients0
Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges0
FEDZIP: A Compression Framework for Communication-Efficient 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