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 65516600 of 6771 papers

TitleStatusHype
Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based InterpretabilityCode0
Towards Energy-Aware Federated Learning on Battery-Powered ClientsCode0
Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated LearningCode0
Capture Global Feature Statistics for One-Shot Federated LearningCode0
Exploring Vacant Classes in Label-Skewed Federated LearningCode0
Privacy and Trust Redefined in Federated Machine LearningCode0
TablePuppet: A Generic Framework for Relational Federated LearningCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Novel clustered federated learning based on local lossCode0
Sharp Bounds for Federated Averaging (Local SGD) and Continuous PerspectiveCode0
Sharp Bounds for Sequential Federated Learning on Heterogeneous DataCode0
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated LearningCode0
Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving FrequencyCode0
Toward Fair Federated Learning under Demographic Disparities and Data ImbalanceCode0
The Cost of Training Machine Learning Models over Distributed Data SourcesCode0
Communication Efficient Private Federated Learning Using DitheringCode0
Communication-Efficient Online Federated Learning Framework for Nonlinear RegressionCode0
Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated LearningCode0
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation SystemCode0
OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated LearningCode0
Federated clustering with GAN-based data synthesisCode0
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data SamplingCode0
Variational Bayes for Federated Continual LearningCode0
E-3SFC: Communication-Efficient Federated Learning with Double-way Features SynthesizingCode0
An effective and efficient green federated learning method for one-layer neural networksCode0
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and FairnessCode0
DynamicFL: Federated Learning with Dynamic Communication Resource AllocationCode0
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
Revealing and Protecting Labels in Distributed TrainingCode0
Dynamically Weighted Federated k-MeansCode0
Reveal Your Images: Gradient Leakage Attack against Unbiased Sampling-Based Secure AggregationCode0
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guaranteesCode0
Federated Black-Box Adaptation for Semantic SegmentationCode0
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced DataCode0
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual ClassificationCode0
Automatic Structured Pruning for Efficient Architecture in Federated LearningCode0
Hybrid Federated Learning: Algorithms and ImplementationCode0
Revisiting Ensembling in One-Shot Federated LearningCode0
On-Demand Sampling: Learning Optimally from Multiple DistributionsCode0
On-Device Collaborative Language Modeling via a Mixture of Generalists and SpecialistsCode0
On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game PerspectiveCode0
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated LearningCode0
Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable AggregationCode0
Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic EnvironmentsCode0
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local UpdatesCode0
Federated Active Learning for Target Domain GeneralisationCode0
CaPriDe Learning: Confidential and Private Decentralized Learning Based on Encryption-Friendly Distillation LossCode0
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated LearningCode0
Federated Hybrid Model Pruning through Loss Landscape ExplorationCode0
Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning PipelinesCode0
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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