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 201250 of 6771 papers

TitleStatusHype
Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare0
Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed EnvironmentsCode0
Evaluating Query Efficiency and Accuracy of Transfer Learning-based Model Extraction Attack in Federated Learning0
Federated Learning: From Theory to Practice0
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge CollaborationCode0
Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated LearningCode0
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers0
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation0
LLM-QFL: Distilling Large Language Model for Quantum Federated LearningCode0
Distribution-Aware Mobility-Assisted Decentralized Federated Learning0
Federated Retrieval-Augmented Generation: A Systematic Mapping Study0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks0
Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation0
AIDRIN 2.0: A Framework to Assess Data Readiness for AI0
Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation0
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare0
ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and ReasoningCode0
Redefining Clustered Federated Learning for System Identification: The Path of ClusterCraft0
Data-Driven Breakthroughs and Future Directions in AI Infrastructure: A Comprehensive Review0
From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling0
Multimodal Online Federated Learning with Modality Missing in Internet of Things0
Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach0
WikiDBGraph: Large-Scale Database Graph of Wikidata for Collaborative Learning0
Reliable Vertical Federated Learning in 5G Core Network ArchitectureCode0
Distributionally Robust Federated Learning with Client Drift Minimization0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions0
Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation0
FedGraM: Defending Against Untargeted Attacks in Federated Learning via Embedding Gram Matrix0
Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned0
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy0
FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments0
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning0
Traceable Black-box Watermarks for Federated Learning0
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy0
FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA0
FLTG: Byzantine-Robust Federated Learning via Angle-Based Defense and Non-IID-Aware Weighting0
RIFLES: Resource-effIcient Federated LEarning via Scheduling0
Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs0
A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis0
γ-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning0
FedHQ: Hybrid Runtime Quantization for Federated Learning0
FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious ClientsCode0
Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners0
Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning0
Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning0
Federated Low-Rank Adaptation for Foundation Models: A SurveyCode0
Joint Graph Estimation and Signal Restoration for Robust Federated Learning0
Random Client Selection on Contrastive Federated Learning for Tabular Data0
Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration0
Heterogeneity-Aware Client Sampling: A Unified Solution for Consistent 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