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 4826–4850 of 6771 papers

Title	Date	Tasks	Status
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories	Nov 1, 2021	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty	Nov 23, 2020	Federated LearningGraph Neural Network	—Unverified
Improving Group Connectivity for Generalization of Federated Deep Learning	Feb 29, 2024	Deep LearningFederated Learning	—Unverified
Improving LoRA in Privacy-preserving Federated Learning	Mar 18, 2024	Computational EfficiencyFederated Learning	—Unverified
Improving Machine Learning Robustness via Adversarial Training	Sep 22, 2023	Federated Learning	—Unverified
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning	Apr 10, 2024	Federated LearningGenerative Adversarial Network	—Unverified
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training	Aug 31, 2023	Federated LearningLesion Segmentation	—Unverified
Improving on-device speaker verification using federated learning with privacy	Aug 6, 2020	Federated LearningMulti-Task Learning	—Unverified
Balancing Privacy and Performance for Private Federated Learning Algorithms	Apr 11, 2023	Federated Learning	—Unverified
Improving the Classification Effect of Clinical Images of Diseases for Multi-Source Privacy Protection	Aug 23, 2024	DiagnosticFederated Learning	—Unverified
Improving the Model Consistency of Decentralized Federated Learning	Feb 8, 2023	Federated Learningmodel	—Unverified
Improving the Robustness of Federated Learning for Severely Imbalanced Datasets	Apr 28, 2022	Federated Learningimbalanced classification	—Unverified
Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem	Jul 7, 2023	FairnessFederated Learning	—Unverified
Incentive-Compatible Federated Learning with Stackelberg Game Modeling	Jan 5, 2025	FairnessFederated Learning	—Unverified
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach	May 16, 2019	Federated Learning	—Unverified
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning	Feb 18, 2022	Federated Learning	—Unverified
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation	Apr 17, 2023	Federated Learning	—Unverified
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective	Nov 20, 2021	Federated Learning	—Unverified
Incentives for Federated Learning: a Hypothesis Elicitation Approach	Jul 21, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners	May 17, 2025	Federated Learning	—Unverified
Incentivizing Federated Learning	May 22, 2022	Federated Learning	—Unverified
Incentivizing High-quality Participation From Federated Learning Agents	Jun 20, 2025	Federated Learning	—Unverified
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization	May 25, 2023	Federated Learning	—Unverified
Incentivizing Inclusive Contributions in Model Sharing Markets	May 5, 2025	Federated LearningInstruction Following	—Unverified
Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge	Mar 6, 2025	Federated LearningPrivacy Preserving	—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