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 2501–2525 of 6771 papers

Title	Date	Tasks	Status
Bayesian data fusion with shared priors	Dec 14, 2022	Bayesian InferenceFederated Learning	—Unverified
Device Scheduling with Fast Convergence for Wireless Federated Learning	Nov 3, 2019	Federated LearningScheduling	—Unverified
Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning	Dec 15, 2023	Federated LearningScheduling	—Unverified
A Bayesian Framework for Clustered Federated Learning	Oct 20, 2024	Federated Learning	—Unverified
Almost Cost-Free Communication in Federated Best Arm Identification	Aug 19, 2022	Federated Learning	—Unverified
Adaptively Weighted Averaging Over-the-Air Computation and Its Application to Distributed Gaussian Process Regression	Jan 29, 2025	Federated LearningGPR	—Unverified
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning	May 31, 2024	Federated LearningModel Poisoning	—Unverified
Device Scheduling for Over-the-Air Federated Learning with Differential Privacy	Oct 31, 2022	Federated LearningScheduling	—Unverified
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning	Jul 23, 2021	Federated LearningScheduling	—Unverified
Bayesian AirComp with Sign-Alignment Precoding for Wireless Federated Learning	Sep 14, 2021	Federated Learning	—Unverified
Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things	Feb 4, 2024	Deep Reinforcement LearningFederated Learning	—Unverified
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation	Jan 4, 2021	Federated LearningLearning Theory	—Unverified
Bayes' capacity as a measure for reconstruction attacks in federated learning	Jun 19, 2024	Federated LearningReconstruction Attack	—Unverified
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning	Dec 1, 2022	Federated LearningReconstruction Attack	—Unverified
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks	Nov 7, 2023	Federated Learning	—Unverified
BayBFed: Bayesian Backdoor Defense for Federated Learning	Jan 23, 2023	backdoor defenseFederated Learning	—Unverified
De-VertiFL: A Solution for Decentralized Vertical Federated Learning	Oct 8, 2024	Federated LearningPrivacy Preserving	—Unverified
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning	Nov 19, 2024	Adversarial AttackBackdoor Attack	—Unverified
Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning	Apr 16, 2025	Federated LearningScheduling	—Unverified
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems	Mar 17, 2025	Computational EfficiencyFederated Learning	—Unverified
Detection of ransomware attacks using federated learning based on the CNN model	May 1, 2024	Federated Learning	—Unverified
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications	Jul 18, 2022	Activity RecognitionAnomaly Detection	—Unverified
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning	Nov 2, 2021	Distributed OptimizationFederated Learning	—Unverified
Detection of Insider Attacks in Distributed Projected Subgradient Algorithms	Jan 18, 2021	Federated Learning	—Unverified
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication	Jul 16, 2024	Anomaly DetectionFederated Learning	—Unverified

Show:10 25 50

← PrevPage 101 of 271Next →

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