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 4076–4100 of 6771 papers

Title	Date	Tasks	Status
Two-Bit Aggregation for Communication Efficient and Differentially Private Federated Learning	Oct 6, 2021	Federated LearningVocal Bursts Valence Prediction	—Unverified
Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attacks	Mar 30, 2025	Federated LearningModel Poisoning	—Unverified
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction	Oct 30, 2022	Federated Learning	—Unverified
UA-PDFL: A Personalized Approach for Decentralized Federated Learning	Dec 16, 2024	Federated LearningPrivacy Preserving	—Unverified
UAV-Aided Multi-Community Federated Learning	Jun 4, 2022	Federated LearningScheduling	—Unverified
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning	Sep 23, 2022	Federated Learning	—Unverified
UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing	Jan 18, 2025	Federated Learning	—Unverified
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach	Jun 29, 2021	Decision MakingDeep Reinforcement Learning	—Unverified
UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis	Jul 5, 2024	Federated Learning	—Unverified
UAV Communications for Sustainable Federated Learning	Mar 20, 2021	Federated Learning	—Unverified
UAV-Enabled Asynchronous Federated Learning	Mar 11, 2024	Federated Learning	—Unverified
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data	Aug 10, 2023	Federated LearningGenerative Adversarial Network	—Unverified
UIFV: Data Reconstruction Attack in Vertical Federated Learning	Jun 18, 2024	Federated LearningReconstruction Attack	—Unverified
Unbounded Gradients in Federated Leaning with Buffered Asynchronous Aggregation	Oct 3, 2022	Federated Learning	—Unverified
Uncertainty-Aware Explainable Federated Learning	Mar 7, 2025	Decision MakingFederated Learning	—Unverified
Uncertainty Minimization for Personalized Federated Semi-Supervised Learning	May 5, 2022	Federated Learning	—Unverified
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor	Feb 20, 2020	Federated LearningQuantization	—Unverified
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning	Oct 13, 2024	Federated Learning	—Unverified
Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review	Aug 26, 2023	Federated LearningSurvey	—Unverified
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks	Sep 8, 2022	Federated Learning	—Unverified
Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders	Apr 14, 2025	Edge-computingFederated Learning	—Unverified
Towards Understanding Adversarial Transferability in Federated Learning	Oct 1, 2023	AttributeFederated Learning	—Unverified
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning	May 15, 2018	Data AugmentationFederated Learning	—Unverified
Understanding Byzantine Robustness in Federated Learning with A Black-box Server	Aug 12, 2024	Federated Learning	—Unverified
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy	Jun 25, 2021	Federated 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