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 50015050 of 6771 papers

TitleStatusHype
Federated Learning Challenges and Opportunities: An Outlook0
Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching0
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?Code1
Personalized Federated Learning via Convex Clustering0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning0
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors0
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training0
Federated Learning with Erroneous Communication Links0
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning0
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning0
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications0
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models0
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters0
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional NetworksCode1
A Secure and Efficient Federated Learning Framework for NLP0
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients0
Gradient Masked Averaging for Federated Learning0
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning0
Towards a Secure and Reliable Federated Learning using Blockchain0
Achieving Personalized Federated Learning with Sparse Local Models0
A dual approach for federated learningCode0
Fast Server Learning Rate Tuning for Coded Federated Dropout0
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization0
Speeding up Heterogeneous Federated Learning with Sequentially Trained SuperclientsCode1
An Efficient and Robust System for Vertically Federated Random Forest0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images0
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees0
Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features0
Towards Multi-Objective Statistically Fair Federated Learning0
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated LearningCode1
Federated Unlearning with Knowledge Distillation0
A Comprehensive Survey on Federated Learning: Concept and Applications0
Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint0
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image SynthesisCode1
FedComm: Federated Learning as a Medium for Covert Communication0
TOFU: Towards Obfuscated Federated Updates by Encoding Weight Updates into Gradients from Proxy Data0
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks0
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges0
Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities0
Minimax Demographic Group Fairness in Federated Learning0
Towards Energy Efficient Distributed Federated Learning for 6G Networks0
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization0
SCOTCH: An Efficient Secure Computation Framework for Secure AggregationCode0
Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost0
Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)0
How to Backdoor HyperNetwork in Personalized Federated Learning?0
Fairness in Federated Learning for Spatial-Temporal Applications0
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks0
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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