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 10011050 of 6771 papers

TitleStatusHype
AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling0
Label Leakage in Federated Inertial-based Human Activity RecognitionCode0
Federated Instrumental Variable Analysis via Federated Generalized Method of Moments0
DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models0
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated LearningCode0
LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments0
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning0
Multimodal Federated Learning With Missing Modalities through Feature Imputation NetworkCode0
Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare0
Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed EnvironmentsCode0
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data0
SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations0
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity0
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things0
Federated Learning-Distillation Alternation for Resource-Constrained IoT0
Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated LearningCode0
Federated Learning: From Theory to Practice0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning0
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge CollaborationCode0
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers0
Distribution-Aware Mobility-Assisted Decentralized Federated Learning0
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks0
LLM-QFL: Distilling Large Language Model for Quantum Federated LearningCode0
Federated Retrieval-Augmented Generation: A Systematic Mapping Study0
Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation0
Multimodal Online Federated Learning with Modality Missing in Internet of Things0
AIDRIN 2.0: A Framework to Assess Data Readiness for AI0
Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach0
ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and ReasoningCode0
Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation0
From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling0
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare0
Data-Driven Breakthroughs and Future Directions in AI Infrastructure: A Comprehensive Review0
Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraft0
WikiDBGraph: Large-Scale Database Graph of Wikidata for Collaborative Learning0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions0
Reliable Vertical Federated Learning in 5G Core Network ArchitectureCode0
Distributionally Robust Federated Learning with Client Drift Minimization0
Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned0
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation0
FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix0
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy0
Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs0
RIFLES: Resource-effIcient Federated LEarning via Scheduling0
FLTG: Byzantine-Robust Federated Learning via Angle-Based Defense and Non-IID-Aware Weighting0
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy0
FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA0
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in 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