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 33013350 of 6771 papers

TitleStatusHype
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction0
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination0
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain0
Fed-Joint: Joint Modeling of Nonlinear Degradation Signals and Failure Events for Remaining Useful Life Prediction using Federated Learning0
FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks0
FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy0
Communication-Efficient Agnostic Federated Averaging0
FedKD: Communication Efficient Federated Learning via Knowledge Distillation0
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning0
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models0
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities0
A Convergence Theory for Federated Average: Beyond Smoothness0
FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing0
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses0
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data0
Layer-wise and Dimension-wise Locally Adaptive Federated Learning0
Fed-LDR: Federated Local Data-infused Graph Creation with Node-centric Model Refinement0
FedNILM: Applying Federated Learning to NILM Applications at the Edge0
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning0
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks0
Communication Efficient Adaptive Model-Driven Quantum Federated Learning0
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout0
A Real-time Contribution Measurement Method for Participants in Federated Learning0
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation0
Distributed Learning for Wi-Fi AP Load Prediction0
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients0
Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles0
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble0
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges0
Federated Neuro-Symbolic Learning0
FedLog: Personalized Federated Classification with Less Communication and More Flexibility0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning0
Distributed Learning on Heterogeneous Resource-Constrained Devices0
Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks0
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks0
FedBA: Non-IID Federated Learning Framework in UAV Networks0
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network0
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation0
Communication Efficiency in Federated Learning: Achievements and Challenges0
FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization0
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder0
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning0
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications0
FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning0
AFed: Algorithmic Fair Federated Learning0
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization0
FedBaF: Federated Learning Aggregation Biased by a Foundation Model0
Communication-Computation Efficient Secure Aggregation for Federated Learning0
FedBABU: Toward Enhanced Representation for Federated Image Classification0
Communication Compression for Distributed Learning without Control Variates0
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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