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 5601–5625 of 6771 papers

Title	Date	Tasks	Status
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning	May 14, 2023	Federated LearningPrediction	—Unverified
Balancing Privacy Protection and Interpretability in Federated Learning	Feb 16, 2023	Federated Learning	—Unverified
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks	Sep 8, 2024	Federated Learning	—Unverified
Balancing Similarity and Complementarity for Federated Learning	May 16, 2024	Federated Learning	—Unverified
Banded Square Root Matrix Factorization for Differentially Private Model Training	May 22, 2024	Federated Learning	—Unverified
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning	Dec 14, 2020	FairnessFederated Learning	—Unverified
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks	Jan 10, 2021	FairnessFederated Learning	—Unverified
Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning	Aug 27, 2024	Data CompressionFederated Learning	—Unverified
Bandwidth Slicing to Boost Federated Learning in Edge Computing	Oct 24, 2019	Edge-computingFederated Learning	—Unverified
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning	May 9, 2023	Bayesian OptimizationFederated Learning	—Unverified
Buffered Asynchronous SGD for Byzantine Learning	Mar 2, 2020	Edge-computingFederated Learning	—Unverified
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning	Nov 2, 2021	Distributed OptimizationFederated Learning	—Unverified
Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning	Apr 16, 2025	Federated LearningScheduling	—Unverified
BayBFed: Bayesian Backdoor Defense for Federated Learning	Jan 23, 2023	backdoor defenseFederated Learning	—Unverified
Bayes' capacity as a measure for reconstruction attacks in federated learning	Jun 19, 2024	Federated LearningReconstruction Attack	—Unverified
Bayesian AirComp with Sign-Alignment Precoding for Wireless Federated Learning	Sep 14, 2021	Federated Learning	—Unverified
A Bayesian Framework for Clustered Federated Learning	Oct 20, 2024	Federated Learning	—Unverified
Bayesian data fusion with shared priors	Dec 14, 2022	Bayesian InferenceFederated Learning	—Unverified
Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shift	May 4, 2025	Federated Learning	—Unverified
Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data sets	Feb 15, 2023	Federated Learning	—Unverified
Bayesian Federated Learning: A Survey	Apr 26, 2023	Federated LearningPrivacy Preserving	—Unverified
Bayesian Federated Learning for Continual Training	Apr 21, 2025	Continual LearningFederated Learning	—Unverified
Bayesian Federated Learning over Wireless Networks	Dec 31, 2020	Federated LearningPrivacy Preserving	—Unverified
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing	Feb 12, 2024	Bayesian InferenceFederated Learning	—Unverified
Federated Learning with Uncertainty via Distilled Predictive Distributions	Jun 15, 2022	Active LearningDecision Making	—Unverified

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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