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 3201–3225 of 6771 papers

Title	Date	Tasks	Status
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity	Jun 21, 2022	Federated Learning	—Unverified
FedBoost: A Communication-Efficient Algorithm for Federated Learning	Jan 1, 2020	Density EstimationFederated Learning	—Unverified
FedFair^3: Unlocking Threefold Fairness in Federated Learning	Jan 29, 2024	FairnessFederated Learning	—Unverified
Training Fair Models in Federated Learning without Data Privacy Infringement	Sep 13, 2021	FairnessFederated Learning	—Unverified
FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining	Apr 8, 2025	Federated Learning	—Unverified
FedBone: Towards Large-Scale Federated Multi-Task Learning	Jun 30, 2023	Federated LearningMulti-Task Learning	—Unverified
Communication-Efficient and Personalized Federated Lottery Ticket Learning	Apr 26, 2021	Federated LearningMulti-Task Learning	—Unverified
Communication-Efficient and Drift-Robust Federated Learning via Elastic Net	Oct 6, 2022	Federated Learning	—Unverified
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning	Mar 25, 2024	Federated Learning	—Unverified
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments	Jun 19, 2024	DiversityFairness	—Unverified
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare	Jun 2, 2021	Activity RecognitionFederated Learning	—Unverified
FedFMC: Sequential Efficient Federated Learning on Non-iid Data	Jun 19, 2020	Federated Learning	—Unverified
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain	Apr 16, 2023	Computational EfficiencyFederated Learning	—Unverified
FedFm: Towards a Robust Federated Learning Approach For Fault Mitigation at the Edge Nodes	Nov 1, 2021	Federated Learning	—Unverified
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning	Nov 22, 2023	Federated Learning	—Unverified
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems	Sep 14, 2023	Federated LearningRecommendation Systems	—Unverified
Fed-Focal Loss for imbalanced data classification in Federated Learning	Nov 12, 2020	ClassificationFederated Learning	—Unverified
FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems	Jul 4, 2021	Federated Learning	—Unverified
FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks	Nov 5, 2024	backdoor defenseFederated Learning	—Unverified
Communication-Efficient Agnostic Federated Averaging	Apr 6, 2021	Federated LearningLanguage Modeling	—Unverified
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning	Aug 7, 2023	Federated LearningReinforcement Learning (RL)	—Unverified
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models	Oct 7, 2024	Federated Learning	—Unverified
Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network	Aug 21, 2022	Federated LearningPrivacy Preserving	—Unverified
Differentially Private Vertical Federated Learning	Nov 13, 2022	Federated LearningVertical Federated Learning	—Unverified
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities	Dec 6, 2024	Federated LearningFew-Shot 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