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 41514200 of 6771 papers

TitleStatusHype
Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity0
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network0
Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data0
REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments0
Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training0
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach0
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges0
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions0
Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients0
Rethinking Client Drift in Federated Learning: A Logit Perspective0
Rethinking Client Reweighting for Selfish Federated Learning0
Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks0
Rethinking Normalization Methods in Federated Learning0
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation0
Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data0
Rethinking the initialization of Momentum in Federated Learning with Heterogeneous Data0
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data0
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks0
Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space0
Review of Mathematical Optimization in Federated Learning0
Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates0
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels0
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks0
Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions0
Revocable Federated Learning: A Benchmark of Federated Forest0
Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning0
Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review0
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices0
Reward-Based 1-bit Compressed Federated Distillation on Blockchain0
Rewarding High-Quality Data via Influence Functions0
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning0
RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning0
Riemannian Federated Learning via Averaging Gradient Stream0
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation0
RIFLES: Resource-effIcient Federated LEarning via Scheduling0
Right Reward Right Time for Federated Learning0
RingFed: Reducing Communication Costs in Federated Learning on Non-IID Data0
RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility0
Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients0
RLSA-PFL: Robust Lightweight Secure Aggregation with Model Inconsistency Detection in Privacy-Preserving Federated Learning0
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties0
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach0
Robust Convergence in Federated Learning through Label-wise Clustering0
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning0
Robust Decentralized Learning with Local Updates and Gradient Tracking0
RobustFed: A Truth Inference Approach for Robust Federated Learning0
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA0
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition0
Robust Federated Learning against Model Perturbation in Edge Networks0
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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