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 1726–1750 of 6771 papers

Title	Date	Tasks	Status
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service	Feb 15, 2024	FairnessFederated Learning	—Unverified
DPCOVID: Privacy-Preserving Federated Covid-19 Detection	Oct 26, 2021	Federated LearningPrivacy Preserving	—Unverified
Blockchain-based Trustworthy Federated Learning Architecture	Aug 16, 2021	FairnessFederated Learning	—Unverified
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation	May 10, 2024	Federated LearningNatural Language Understanding	—Unverified
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates	Jun 13, 2021	BenchmarkingFederated Learning	—Unverified
DPP-based Client Selection for Federated Learning with Non-IID Data	Mar 30, 2023	Federated Learning	—Unverified
DP-REC: Private & Communication-Efficient Federated Learning	Nov 9, 2021	Federated Learning	—Unverified
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing	Dec 30, 2024	Edge-computingFairness	—Unverified
DPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learning	Oct 19, 2024	Contribution AssessmentFederated Learning	—Unverified
DPZV: Elevating the Tradeoff between Privacy and Utility in Zeroth-Order Vertical Federated Learning	Feb 27, 2025	Federated LearningVertical Federated Learning	—Unverified
DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering	Nov 7, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks	Jun 19, 2024	Federated Learning	—Unverified
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences	Jul 13, 2025	Federated LearningReconstruction Attack	—Unverified
DRAG: Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data	Sep 4, 2023	Federated Learning	—Unverified
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing	Oct 6, 2022	Federated Learning	—Unverified
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup	Apr 16, 2022	Drug DiscoveryFederated Learning	—Unverified
Drift-Aware Federated Learning: A Causal Perspective	Mar 12, 2025	Federated Learning	—Unverified
Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification	Jun 19, 2023	Federated Learning	—Unverified
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee	Nov 3, 2020	Distributed ComputingFairness	—Unverified
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices	Nov 12, 2024	Federated Learning	—Unverified
A blockchain-orchestrated Federated Learning architecture for healthcare consortia	Oct 12, 2019	Federated LearningPrivacy Preserving	—Unverified
Efficient Federated Learning over Multiple Access Channel with Differential Privacy Constraints	May 15, 2020	Federated LearningQuantization	—Unverified
Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality	Nov 12, 2024	Federated LearningPrivacy Preserving	—Unverified
Blockchain-enabled Trustworthy Federated Unlearning	Jan 29, 2024	Federated Learning	—Unverified
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout	Jul 14, 2025	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