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 42014250 of 6771 papers

TitleStatusHype
Find Your Friends: Personalized Federated Learning with the Right Collaborators0
Anomaly Detection via Federated Learning0
Privacy of federated QR decomposition using additive secure multiparty computation0
Aergia: Leveraging Heterogeneity in Federated Learning SystemsCode0
Cross-client Label Propagation for Transductive and Semi-Supervised Federated LearningCode0
Federated Continual Learning for Text Classification via Selective Inter-client TransferCode0
Label Inference Attacks Against Vertical Federated LearningCode1
Few-Shot Model Agnostic Federated LearningCode1
FedBA: Non-IID Federated Learning Framework in UAV Networks0
A Survey on Heterogeneous Federated Learning0
On the Performance of Gradient Tracking with Local Updates0
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare SettingsCode2
Collaborative Domain Blocking: Using federated NLP To Detect Malicious Domains0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems0
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings0
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning0
Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent0
Time Minimization in Hierarchical Federated Learning0
Rethinking Normalization Methods in Federated Learning0
FedGraph: an Aggregation Method from Graph Perspective0
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated LearningCode1
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing0
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data0
Communication-Efficient and Drift-Robust Federated Learning via Elastic Net0
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations0
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated LearningCode1
Decentralized Hyper-Gradient Computation over Time-Varying Directed NetworksCode0
FedMT: Federated Learning with Mixed-type Labels0
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance SamplingCode0
Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning0
Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase0
Invariant Aggregator for Defending against Federated Backdoor Attacks0
Group Personalized Federated Learning0
TabLeak: Tabular Data Leakage in Federated LearningCode1
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning0
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning0
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving DesensitizationCode2
Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach0
Federated Graph-based Networks with Shared Embedding0
PersA-FL: Personalized Asynchronous Federated Learning0
Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests0
Unbounded Gradients in Federated Leaning with Buffered Asynchronous Aggregation0
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms0
Federated Domain Generalization for Image Recognition via Cross-Client Style TransferCode1
Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning0
SAGDA: Achieving O(ε^-2) Communication Complexity in Federated Min-Max Learning0
FLCert: Provably Secure Federated Learning against Poisoning Attacks0
Federated Representation Learning via Maximal Coding Rate Reduction0
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph0
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated LearningCode1
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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