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 45514600 of 6771 papers

TitleStatusHype
Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift0
Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training0
Task-Agnostic Federated Learning0
Task Arithmetic Through The Lens Of One-Shot Federated Learning0
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning0
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence0
Technical Report: Aggregation on Learnable Manifolds for Asynchronous Federated Optimization0
Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment0
Technical Report: On the Convergence of Gossip Learning in the Presence of Node Inaccessibility0
Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning0
TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing0
TempCharBERT: Keystroke Dynamics for Continuous Access Control Based on Pre-trained Language Models0
Tempora-Fusion: Time-Lock Puzzle with Efficient Verifiable Homomorphic Linear Combination0
Temporal Analysis of Adversarial Attacks in Federated Learning0
Temporal Gradient Inversion Attacks with Robust Optimization0
Ten Challenging Problems in Federated Foundation Models0
Tensor Decomposition based Personalized Federated Learning0
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model0
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks0
Text-driven Prompt Generation for Vision-Language Models in Federated Learning0
DoCoFL: Downlink Compression for Cross-Device Federated Learning0
DoStoVoQ: Doubly Stochastic Voronoi Vector Quantization SGD for Federated Learning0
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus0
The Applicability of Federated Learning to Official Statistics0
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems0
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation0
The Built-In Robustness of Decentralized Federated Averaging to Bad Data0
The Copycat Perceptron: Smashing Barriers Through Collective Learning0
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning0
FedEGG: Federated Learning with Explicit Global Guidance0
The Effect of Quantization in Federated Learning: A Rényi Differential Privacy Perspective0
The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting0
The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning0
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning0
The Future of Digital Health with Federated Learning0
The Future of Large Language Model Pre-training is Federated0
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning0
Quality Inference in Federated Learning with Secure Aggregation0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication0
The Hidden Adversarial Vulnerabilities of Medical Federated Learning0
The Impact Analysis of Delays in Asynchronous Federated Learning with Data Heterogeneity for Edge Intelligence0
The Impact of Cut Layer Selection in Split Federated Learning0
The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies0
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning0
The Key of Parameter Skew in Federated Learning0
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning0
The Need for Advanced Intelligence in NFV Management and Orchestration0
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory0
Theoretically Principled Federated Learning for Balancing Privacy and Utility0
Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models0
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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