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 701750 of 6771 papers

TitleStatusHype
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision TransformersCode1
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated LearningCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous ClientsCode1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
Bayesian Framework for Gradient LeakageCode1
FedScale: Benchmarking Model and System Performance of Federated Learning at ScaleCode1
Agnostic Federated LearningCode1
FedSim: Similarity guided model aggregation for Federated LearningCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
Fedstellar: A Platform for Decentralized Federated LearningCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular DataCode1
FedTP: Federated Learning by Transformer PersonalizationCode1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian RegularizationCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Ferrari: Federated Feature Unlearning via Optimizing Feature SensitivityCode1
FeSViBS: Federated Split Learning of Vision Transformer with Block SamplingCode1
Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated LearningCode1
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image ClassificationCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
FLAME: Differentially Private Federated Learning in the Shuffle ModelCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious ClientsCode1
Flexible Clustered Federated Learning for Client-Level Data Distribution ShiftCode1
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated LearningCode1
Decentralized Federated Learning: A Segmented Gossip ApproachCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
FL_PyTorch: optimization research simulator for federated learningCode1
FLTracer: Accurate Poisoning Attack Provenance in Federated LearningCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness MatchingCode1
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model FusionCode1
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical ImagingCode1
Generative Autoregressive Transformers for Model-Agnostic Federated MRI ReconstructionCode1
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated LearningCode1
GIFD: A Generative Gradient Inversion Method with Feature Domain OptimizationCode1
GPT-FL: Generative Pre-trained Model-Assisted Federated LearningCode1
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant MatrixCode1
Gradient-Leakage Resilient Federated LearningCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
GPFedRec: Graph-guided Personalization for Federated RecommendationCode1
Greedy Shapley Client Selection for Communication-Efficient Federated LearningCode1
Group Distributionally Robust Dataset Distillation with Risk MinimizationCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
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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