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 4926–4950 of 6771 papers

Title	Date	Tasks	Status
Kalman Filter Aided Federated Koopman Learning	Jul 7, 2025	Federated Learning	—Unverified
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning	Apr 18, 2024	Federated LearningKnowledge Distillation	—Unverified
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients	May 16, 2023	Federated Learning	—Unverified
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach	Apr 3, 2023	Federated LearningPrivacy Preserving	—Unverified
Collaborative Learning in Kernel-based Bandits for Distributed Users	Jul 16, 2022	Federated Learning	—Unverified
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning	Sep 30, 2022	Federated LearningPrivacy Preserving	—Unverified
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification	Oct 6, 2023	Anomaly DetectionFederated Learning	—Unverified
KnFu: Effective Knowledge Fusion	Mar 18, 2024	Federated LearningKnowledge Distillation	—Unverified
Knowledge Adaptation as Posterior Correction	Jun 17, 2025	Federated Learning	—Unverified
Knowledge-aided Federated Learning for Energy-limited Wireless Networks	Sep 25, 2022	Federated LearningScheduling	—Unverified
Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data	Mar 5, 2025	FairnessFederated Learning	—Unverified
Knowledge Distillation for Federated Learning: a Practical Guide	Nov 9, 2022	Federated LearningKnowledge Distillation	—Unverified
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions	Jun 16, 2024	Federated LearningKnowledge Distillation	—Unverified
Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework	Feb 5, 2020	Federated LearningPrivacy Preserving	—Unverified
Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions	Dec 27, 2022	Data AugmentationFederated Learning	—Unverified
Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning	Mar 28, 2022	Federated Learning	—Unverified
KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting	Apr 19, 2024	Federated Learning	—Unverified
Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity	May 26, 2025	DiversityFederated Learning	—Unverified
Label driven Knowledge Distillation for Federated Learning with non-IID Data	Sep 29, 2022	Federated LearningKnowledge Distillation	—Unverified
Label Leakage and Protection from Forward Embedding in Vertical Federated Learning	Mar 2, 2022	Federated LearningVertical Federated Learning	—Unverified
Label-shift robust federated feature screening for high-dimensional classification	May 31, 2025	Computational EfficiencyFederated Learning	—Unverified
LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation	May 27, 2024	Federated LearningPrivacy Preserving	—Unverified
LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples	Oct 24, 2024	Federated LearningPrivacy Preserving	—Unverified
LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments	May 26, 2025	Federated Learning	—Unverified
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence	Jul 6, 2023	Code GenerationFederated Learning	—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