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 37513800 of 6771 papers

TitleStatusHype
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning0
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks0
Boosting Federated Learning Convergence with Prototype Regularization0
Fairness-Aware Client Selection for Federated Learning0
Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks0
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated LearningCode0
Eliminating Label Leakage in Tree-Based Vertical Federated Learning0
Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior0
Graph Federated Learning Based on the Decentralized Framework0
A Federated learning model for Electric Energy management using Blockchain Technology0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing0
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey0
Integration of Large Language Models and Federated Learning0
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning0
FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning0
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels0
A Secure Aggregation for Federated Learning on Long-Tailed Data0
Privacy-preserving patient clustering for personalized federated learningCode0
DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning0
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise0
Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices0
Federated Learning-Empowered AI-Generated Content in Wireless Networks0
Population Expansion for Training Language Models with Private Federated Learning0
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout0
Layer-wise Linear Mode ConnectivityCode0
Online Distributed Learning with Quantized Finite-Time Coordination0
Tackling Computational Heterogeneity in FL: A Few Theoretical Insights0
FDAPT: Federated Domain-adaptive Pre-training for Language ModelsCode0
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators0
FedYolo: Augmenting Federated Learning with Pretrained Transformers0
DBFed: Debiasing Federated Learning Framework based on Domain-Independent0
Advances and Challenges in Meta-Learning: A Technical Review0
Fairness-aware Federated Minimax Optimization with Convergence Guarantee0
FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks0
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles0
Federated Learning over a Wireless Network: Distributed User Selection through Random Access0
Federated Unlearning via Active Forgetting0
Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem0
Meta Federated Reinforcement Learning for Distributed Resource Allocation0
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence0
FLuID: Mitigating Stragglers in Federated Learning using Invariant DropoutCode0
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal PerspectivesCode0
Personalized Federated Learning via Amortized Bayesian Meta-Learning0
SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems0
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile DevicesCode0
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks0
Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions0
Defending Against Poisoning Attacks in Federated Learning with Blockchain0
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory0
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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