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 49515000 of 6771 papers

TitleStatusHype
Latency Analysis of Consortium Blockchained Federated Learning0
Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning0
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing0
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation0
Layer-wise Adaptive Model Aggregation for Scalable Federated Learning0
Layer-wise Characterization of Latent Information Leakage in Federated Learning0
Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data0
LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data0
LDP-Fed: Federated Learning with Local Differential Privacy0
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy0
LEAF: A Benchmark for Federated Settings0
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning0
A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning0
Learn Electronic Health Records by Fully Decentralized Federated Learning0
Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks0
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning0
Learning and Generalization with Mixture Data0
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets0
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients0
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data0
Learning discrete distributions: user vs item-level privacy0
Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalization0
Learning Federated Representations and Recommendations with Limited Negatives0
Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization0
Learning from straggler clients in federated learning0
Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks0
Learnings from Federated Learning in the Real world0
Learning To Collaborate in Decentralized Learning of Personalized Models0
Learning to Detect Malicious Clients for Robust Federated Learning0
Learning to Generate Image Embeddings with User-level Differential Privacy0
Learning Tokenization in Private Federated Learning with Sub-Word Model Sampling0
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images0
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels0
Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data0
Learn More by Using Less: Distributed Learning with Energy-Constrained Devices0
Learn to Forget: Machine Unlearning via Neuron Masking0
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning0
LEGO: Language Model Building Blocks0
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning0
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning0
Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance0
Leveraging feature communication in federated learning for remote sensing image classification0
Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks0
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures0
Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks0
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality0
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning0
Leveraging Function Space Aggregation for Federated Learning at Scale0
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training0
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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