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 43514400 of 6771 papers

TitleStatusHype
Joint Privacy Enhancement and Quantization in Federated LearningCode1
FedOS: using open-set learning to stabilize training in federated learningCode0
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering0
Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network0
Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images0
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning0
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data DistributionCode1
A Review of Federated Learning in Energy Systems0
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation0
Almost Cost-Free Communication in Federated Best Arm Identification0
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning0
Federated Learning of Neural ODE Models with Different Iteration Counts0
A Hybrid Self-Supervised Learning Framework for Vertical Federated LearningCode1
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data0
FedMR: Fedreated Learning via Model Recombination0
Knowledge-Injected Federated LearningCode0
FedPerm: Private and Robust Federated Learning by Parameter Permutation0
Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model FusionCode0
Federated Quantum Natural Gradient Descent for Quantum Federated Learning0
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public TransportationCode1
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation0
Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated LearningCode0
Trustworthy Federated Learning via Blockchain0
Practical Vertical Federated Learning with Unsupervised Representation LearningCode1
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives0
A Knowledge Distillation-Based Backdoor Attack in Federated Learning0
Dropout is NOT All You Need to Prevent Gradient LeakageCode0
A Fast Blockchain-based Federated Learning Framework with Compressed Communications0
A Modified UDP for Federated Learning Packet Transmissions0
Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone0
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated LearningCode1
Fast Heterogeneous Federated Learning with Hybrid Client Selection0
Application of federated learning in manufacturing0
Combining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study0
PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning0
Towards Energy-Aware Federated Learning on Battery-Powered ClientsCode0
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets0
Federated Adversarial Learning: A Framework with Convergence Analysis0
Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning0
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model0
Distributed Contrastive Learning for Medical Image Segmentation0
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions0
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity0
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning0
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange0
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?0
A New Implementation of Federated Learning for Privacy and Security Enhancement0
Asynchronous Federated Learning for Edge-assisted Vehicular NetworksCode1
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized 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