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 3426–3450 of 6771 papers

Title	Date	Tasks	Status	Hype
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization	May 22, 2023	Federated LearningScheduling	—Unverified	0
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning	May 22, 2023	Federated Learning	CodeCode Available	0
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions	May 22, 2023	AttributeFederated Learning	CodeCode Available	0
Federated Learning of Medical Concepts Embedding using BEHRT	May 22, 2023	Federated LearningLanguage Modeling	CodeCode Available	0
One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes	May 21, 2023	Federated LearningKnowledge Distillation	—Unverified	0
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter	May 21, 2023	ClusteringFederated Learning	CodeCode Available	1
Confidence-aware Personalized Federated Learning via Variational Expectation Maximization	May 21, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Can Public Large Language Models Help Private Cross-device Federated Learning?	May 20, 2023	Federated Learning	—Unverified	0
Privacy in Multimodal Federated Human Activity Recognition	May 20, 2023	Activity RecognitionFederated Learning	—Unverified	0
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape	May 19, 2023	Federated Learning	CodeCode Available	1
Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation	May 19, 2023	Federated Learning	—Unverified	0
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods	May 19, 2023	FairnessFederated Learning	CodeCode Available	0
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection	May 19, 2023	Autonomous DrivingClustering	—Unverified	0
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy	May 19, 2023	Federated Learning	—Unverified	0
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models	May 19, 2023	Federated LearningLifelong learning	—Unverified	0
Trustworthy Federated Learning: A Survey	May 19, 2023	FairnessFederated Learning	—Unverified	0
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages	May 18, 2023	DiagnosticFederated Learning	—Unverified	0
Efficient Vertical Federated Learning with Secure Aggregation	May 18, 2023	Federated LearningFraud Detection	—Unverified	0
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping	May 18, 2023	Federated Learning	—Unverified	0
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination	May 18, 2023	Federated Learning	—Unverified	0
Mitigating Group Bias in Federated Learning: Beyond Local Fairness	May 17, 2023	FairnessFederated Learning	CodeCode Available	0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime	May 17, 2023	Computational EfficiencyDistributed Optimization	—Unverified	0
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients	May 16, 2023	Federated Learning	—Unverified	0
Faster Federated Learning with Decaying Number of Local SGD Steps	May 16, 2023	Federated Learning	—Unverified	0
Smart Policy Control for Securing Federated Learning Management System	May 16, 2023	Federated LearningManagement	—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