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 9511000 of 6771 papers

TitleStatusHype
FedAPM: Federated Learning via ADMM with Partial Model PersonalizationCode0
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems0
Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning0
Communication Efficient Adaptive Model-Driven Quantum Federated Learning0
GCFL: A Gradient Correction-based Federated Learning Framework for Privacy-preserving CPSS0
Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning0
Gradient Inversion Attacks on Parameter-Efficient Fine-TuningCode0
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning0
Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback0
QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality0
Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles0
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review0
Reconciling Hessian-Informed Acceleration and Scalar-Only Communication for Efficient Federated Zeroth-Order Fine-Tuning0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling0
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation0
FORLA:Federated Object-centric Representation Learning with Slot Attention0
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models0
Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity0
Sociodynamics-inspired Adaptive Coalition and Client Selection in Federated Learning0
Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning0
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA0
Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare0
Label-shift robust federated feature screening for high-dimensional classification0
Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case studyCode0
Towards Graph-Based Privacy-Preserving Federated Learning: ModelNet -- A ResNet-based Model Classification Dataset0
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection0
Robust Federated Learning against Model Perturbation in Edge Networks0
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization0
The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian SketchesCode0
An Empirical Study of Federated Prompt Learning for Vision Language Model0
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats0
Loss-Guided Model Sharing and Local Learning Correction in Decentralized Federated Learning for Crop Disease Classification0
Adaptive Deadline and Batch Layered Synchronized Federated Learning0
Federated Unsupervised Semantic Segmentation0
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment0
Deep Modeling and Optimization of Medical Image ClassificationCode0
Accelerated Training of Federated Learning via Second-Order Methods0
Measuring Participant Contributions in Decentralized Federated Learning0
Position: Federated Foundation Language Model Post-Training Should Focus on Open-Source Models0
FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient EstimationCode0
Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections0
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning0
PathFL: Multi-Alignment Federated Learning for Pathology Image SegmentationCode0
Inclusive, Differentially Private Federated Learning for Clinical Data0
DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated IndustriesCode0
Privacy-Preserving Chest X-ray Report Generation via Multimodal Federated Learning with ViT and GPT-20
Addressing Data Quality Decompensation in Federated Learning via Dynamic Client SelectionCode0
AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling0
DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models0
Label Leakage in Federated Inertial-based Human Activity RecognitionCode0
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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