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 52515300 of 6771 papers

TitleStatusHype
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy0
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning0
FedEmbed: Personalized Private Federated Learning0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks0
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!0
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates0
FLAME: Federated Learning Across Multi-device Environments0
Cross-Silo Heterogeneous Model Federated Multitask LearningCode0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning0
Federated Stochastic Gradient Descent Begets Self-Induced Momentum0
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model0
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams0
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices0
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy0
Towards Verifiable Federated Learning0
Federated Graph Neural Networks: Overview, Techniques and Challenges0
Federated-Learning-Based Anomaly Detection for IoT Security Attacks0
Architecture Agnostic Federated Learning for Neural Networks0
Do Gradient Inversion Attacks Make Federated Learning Unsafe?0
FLHub: a Federated Learning model sharing service0
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning0
On the Convergence of Clustered Federated LearningCode0
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning0
On Federated Learning with Energy Harvesting Clients0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction0
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling0
PPA: Preference Profiling Attack Against Federated Learning0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection0
Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning0
Vertical Federated Learning: Challenges, Methodologies and Experiments0
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data0
Learnings from Federated Learning in the Real world0
SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees0
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks0
Preserving Privacy and Security in Federated Learning0
ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning0
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks0
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning0
Fabricated Flips: Poisoning Federated Learning without Data0
Lossy Gradient Compression: How Much Accuracy Can One Bit Buy?Code0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning0
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting0
A Coalition Formation Game Approach for Personalized Federated Learning0
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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