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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3201–3225 of 6771 papers

Title	Date	Tasks	Status	Hype
FedDefender: Client-Side Attack-Tolerant Federated Learning	Jul 18, 2023	Federated LearningKnowledge Distillation	CodeCode Available	1
Privacy-preserving patient clustering for personalized federated learning	Jul 17, 2023	ClusteringFederated Learning	CodeCode Available	0
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels	Jul 17, 2023	Federated LearningPseudo Label	—Unverified	0
A Secure Aggregation for Federated Learning on Long-Tailed Data	Jul 17, 2023	Federated Learning	—Unverified	0
FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning	Jul 17, 2023	Federated Learning	—Unverified	0
DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning	Jul 16, 2023	Federated LearningScheduling	—Unverified	0
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning	Jul 16, 2023	Continual LearningFederated Learning	CodeCode Available	2
Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices	Jul 14, 2023	Automatic Speech RecognitionFederated Learning	—Unverified	0
Population Expansion for Training Language Models with Private Federated Learning	Jul 14, 2023	Domain AdaptationFederated Learning	—Unverified	0
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning	Jul 14, 2023	Federated LearningRepresentation Learning	CodeCode Available	1
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise	Jul 14, 2023	Federated Learning	—Unverified	0
Federated Learning-Empowered AI-Generated Content in Wireless Networks	Jul 14, 2023	Federated Learning	—Unverified	0
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout	Jul 14, 2023	Bayesian InferenceFederated Learning	—Unverified	0
Layer-wise Linear Mode Connectivity	Jul 13, 2023	Federated LearningLinear Mode Connectivity	CodeCode Available	0
Online Distributed Learning with Quantized Finite-Time Coordination	Jul 13, 2023	Federated Learning	—Unverified	0
FDAPT: Federated Domain-adaptive Pre-training for Language Models	Jul 12, 2023	Computational EfficiencyFederated Learning	CodeCode Available	0
Tackling Computational Heterogeneity in FL: A Few Theoretical Insights	Jul 12, 2023	Federated Learning	—Unverified	0
Locally Adaptive Federated Learning	Jul 12, 2023	Federated Learning	CodeCode Available	1
Benchmarking Algorithms for Federated Domain Generalization	Jul 11, 2023	BenchmarkingDiversity	CodeCode Available	1
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators	Jul 11, 2023	Federated Learning	—Unverified	0
FedYolo: Augmenting Federated Learning with Pretrained Transformers	Jul 10, 2023	Federated Learning	—Unverified	0
DBFed: Debiasing Federated Learning Framework based on Domain-Independent	Jul 10, 2023	FairnessFederated Learning	—Unverified	0
Advances and Challenges in Meta-Learning: A Technical Review	Jul 10, 2023	Continual LearningDomain Adaptation	—Unverified	0
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles	Jul 10, 2023	Federated Learningparameter estimation	—Unverified	0
Fairness-aware Federated Minimax Optimization with Convergence Guarantee	Jul 10, 2023	FairnessFederated Learning	—Unverified	0

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All datasets Cityscapes heterogeneous Landmarks-User-160k CIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SiloBN + ASAM	mIoU	49.75	—	Unverified
2	SiloBN + SAM	mIoU	49.1	—	Unverified
3	SiloBN	mIoU	45.96	—	Unverified
4	FedSAM + SWA	mIoU	43.42	—	Unverified
5	FedASAM + SWA	mIoU	43.02	—	Unverified
6	FedAvg + SWA	mIoU	42.48	—	Unverified
7	FedASAM	mIoU	42.27	—	Unverified
8	FedSAM	mIoU	41.22	—	Unverified
9	FedAvg	mIoU	38.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	Acc@1-1262Clients	68.32	—	Unverified
2	FedSAM + SWA	Acc@1-1262Clients	68.12	—	Unverified
3	FedAvg + SWA	Acc@1-1262Clients	67.52	—	Unverified
4	FedASAM	Acc@1-1262Clients	64.23	—	Unverified
5	FedSAM	Acc@1-1262Clients	63.72	—	Unverified
6	FedAvg	Acc@1-1262Clients	61.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.64	—	Unverified
2	FedASAM	ACC@1-100Clients	39.76	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.51	—	Unverified
4	FedSAM	ACC@1-100Clients	36.93	—	Unverified
5	FedAvg	ACC@1-100Clients	36.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	41.62	—	Unverified
2	FedASAM	ACC@1-100Clients	40.81	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.24	—	Unverified
4	FedAvg	ACC@1-100Clients	38.59	—	Unverified
5	FedSAM	ACC@1-100Clients	38.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.72	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	46.76	—	Unverified
3	FedASAM	ACC@1-100Clients	46.58	—	Unverified
4	FedSAM	ACC@1-100Clients	44.84	—	Unverified
5	FedAvg	ACC@1-100Clients	41.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.27	—	Unverified
2	FedASAM	ACC@1-100Clients	47.78	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	46.47	—	Unverified
4	FedSAM	ACC@1-100Clients	46.05	—	Unverified
5	FedAvg	ACC@1-100Clients	42.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	49.17	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	47.96	—	Unverified
3	FedASAM	ACC@1-100Clients	45.61	—	Unverified
4	FedSAM	ACC@1-100Clients	44.73	—	Unverified
5	FedAvg	ACC@1-100Clients	40.43	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.01	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	39.3	—	Unverified
3	FedASAM	ACC@1-100Clients	36.04	—	Unverified
4	FedSAM	ACC@1-100Clients	31.04	—	Unverified
5	FedAvg	ACC@1-100Clients	30.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.97	—	Unverified
2	FedASAM + SWA	ACC@1-100Clients	54.79	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.67	—	Unverified
4	FedSAM	ACC@1-100Clients	53.39	—	Unverified
5	FedAvg	ACC@1-100Clients	50.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.5	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	54.36	—	Unverified
3	FedASAM + SWA	ACC@1-100Clients	54.1	—	Unverified
4	FedSAM	ACC@1-100Clients	53.97	—	Unverified
5	FedAvg	ACC@1-100Clients	50.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.81	—	Unverified
2	FedSAM	ACC@1-100Clients	54.01	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.9	—	Unverified
4	FedASAM + SWA	ACC@1-100Clients	53.86	—	Unverified
5	FedAvg	ACC@1-100Clients	49.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AdaBest	Average Top-1 Accuracy	56.2	—	Unverified