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 17511800 of 6771 papers

TitleStatusHype
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Training Diffusion Models with Federated Learning0
Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing0
Federated Learning with Limited Node Labels0
Federated Learning with a Single Shared ImageCode0
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty EstimationCode0
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning0
UIFV: Data Reconstruction Attack in Vertical Federated Learning0
Synergizing Foundation Models and Federated Learning: A SurveyCode0
Save It All: Enabling Full Parameter Tuning for Federated Large Language Models via Cycle Block Gradient DescentCode1
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI ModalitiesCode1
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification0
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
When NOMA Meets AIGC: Enhanced Wireless Federated Learning0
Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks0
Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives0
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions0
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality0
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA0
Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data0
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models0
Byzantine-Robust Decentralized Federated Learning0
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks0
Recent Advances in Federated Learning Driven Large Language Models: A Survey on Architecture, Performance, and Security0
Federated Learning with Flexible Architectures0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature AugmentationCode0
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation0
IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content0
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated LearningCode0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data0
A deep cut into Split Federated Self-supervised LearningCode0
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach0
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests0
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm0
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning0
Federated learning in food research0
Decentralized Personalized Federated Learning0
Optimisation of federated learning settings under statistical heterogeneity variations0
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender SystemCode0
Federated LoRA with Sparse CommunicationCode0
When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain0
Federated Representation Learning in the Under-Parameterized RegimeCode0
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language ModelsCode3
1-D CNN-Based Online Signature Verification with Federated Learning0
R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional GradientsCode0
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning0
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated LearningCode0
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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