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 45014550 of 6771 papers

TitleStatusHype
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class ImbalanceCode1
An Empirical Study of Personalized Federated LearningCode1
Differentially Private Federated Combinatorial Bandits with Constraints0
APPFLChain: A Privacy Protection Distributed Artificial-Intelligence Architecture Based on Federated Learning and Consortium Blockchain0
Cross-Silo Federated Learning: Challenges and Opportunities0
FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learning0
FLVoogd: Robust And Privacy Preserving Federated Learning0
zPROBE: Zero Peek Robustness Checks for Federated Learning0
"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated LearningCode0
Using Autoencoders on Differentially Private Federated Learning GANsCode0
FEATHERS: Federated Architecture and Hyperparameter Search0
MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution0
Data Leakage in Federated AveragingCode0
Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks0
EFFGAN: Ensembles of fine-tuned federated GANs0
Efficient Adaptive Federated Optimization of Federated Learning for IoT0
On the Importance and Applicability of Pre-Training for Federated LearningCode0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets0
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices0
Federated Latent Class Regression for Hierarchical Data0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks0
FedorAS: Federated Architecture Search under system heterogeneity0
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus0
How to Combine Variational Bayesian Networks in Federated LearningCode1
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application0
Personalized Subgraph Federated LearningCode1
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning0
sqSGD: Locally Private and Communication Efficient Federated Learning0
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates0
WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning0
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity0
Robust One Round Federated Learning with Predictive Space Bayesian InferenceCode0
SoteriaFL: A Unified Framework for Private Federated Learning with Communication CompressionCode0
Shuffle Gaussian Mechanism for Differential PrivacyCode1
Communication-Efficient Federated Learning With Data and Client Heterogeneity0
Mitigating Data Heterogeneity in Federated Learning with Data AugmentationCode1
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm0
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data SynthesisCode0
Decoupled Federated Learning for ASR with Non-IID Data0
Secure Embedding Aggregation for Federated Representation Learning0
Pisces: Efficient Federated Learning via Guided Asynchronous TrainingCode1
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated LearningCode1
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized HealthcareCode2
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
Federated learning with incremental clustering for heterogeneous data0
On Privacy and Personalization in Cross-Silo Federated LearningCode1
Personalized Federated Learning via Variational Bayesian InferenceCode1
Using adversarial images to improve outcomes of federated learning for non-IID data0
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data0
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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