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 1901–1925 of 6771 papers

Title	Date	Tasks	Status
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning	May 2, 2023	Backdoor AttackFederated Learning	—Unverified
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning	Oct 7, 2022	BlockingFederated Learning	—Unverified
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data	Jul 15, 2025	Federated LearningTime Series	—Unverified
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework	Jan 1, 2022	Federated LearningTrajectory Prediction	—Unverified
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning	Feb 25, 2021	Federated LearningMeta-Learning	—Unverified
EM for Mixture of Linear Regression with Clustered Data	Aug 22, 2023	Federated Learningregression	—Unverified
EMO: Edge Model Overlays to Scale Model Size in Federated Learning	Apr 1, 2025	Federated Learningmodel	—Unverified
Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs	May 11, 2025	Emotion RecognitionFairness	—Unverified
Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI	Mar 20, 2025	BenchmarkingFairness	—Unverified
Empirical Studies of Institutional Federated Learning For Natural Language Processing	Nov 1, 2020	Federated LearningGPU	—Unverified
Employing Federated Learning for Training Autonomous HVAC Systems	May 1, 2024	Federated Learningreinforcement-learning	—Unverified
Employing Layerwised Unsupervised Learning to Lessen Data and Loss Requirements in Forward-Forward Algorithms	Apr 23, 2024	Deep LearningFederated Learning	—Unverified
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers	Nov 6, 2023	Federated Learning	—Unverified
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research	Jun 25, 2025	Data IntegrationDimensionality Reduction	—Unverified
Empowering Federated Learning for Massive Models with NVIDIA FLARE	Feb 12, 2024	Federated Learning	—Unverified
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics	Apr 15, 2024	Federated Learning	—Unverified
D2p-fed:Differentially Private Federated Learning with Efficient Communication	Jan 1, 2021	Federated Learning	—Unverified
Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning	Apr 7, 2021	Decision Makingenergy trading	—Unverified
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning	Jan 28, 2023	Continual LearningFederated Learning	—Unverified
ATM: Improving Model Merging by Alternating Tuning and Merging	Nov 5, 2024	Federated LearningMulti-Task Learning	—Unverified
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision	Sep 7, 2023	Edge-computingFederated Learning	—Unverified
Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective	Jun 22, 2021	Federated Learning	—Unverified
Enabling On-Device Training of Speech Recognition Models with Federated Dropout	Oct 7, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications	Sep 21, 2023	Federated Learning	—Unverified
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy	Jul 5, 2022	Federated LearningKnowledge Distillation	—Unverified

Show:10 25 50

← PrevPage 77 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