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 6251–6275 of 6771 papers

Title	Date	Tasks	Status
Differentially-Private Multi-Tier Federated Learning	Jan 21, 2024	Federated Learning	—Unverified
Differential Privacy Meets Federated Learning under Communication Constraints	Jan 28, 2021	Federated LearningModel Compression	—Unverified
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates	Mar 12, 2025	Federated LearningPersonalized Federated Learning	—Unverified
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection	Feb 16, 2024	Federated LearningPrivacy Preserving	—Unverified
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation	Feb 22, 2022	Federated Learning	—Unverified
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks	Dec 5, 2022	FairnessFederated Learning	—Unverified
Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations	May 22, 2024	Federated Learning	—Unverified
Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learning	Jan 4, 2025	Federated Learning	—Unverified
Digital Ethics in Federated Learning	Oct 4, 2023	EthicsFairness	—Unverified
Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning	Mar 10, 2023	Federated LearningKnowledge Distillation	—Unverified
Digital versus Analog Transmissions for Federated Learning over Wireless Networks	Feb 15, 2024	Federated Learning	—Unverified
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation	Oct 13, 2022	Federated Learning	—Unverified
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning	Mar 11, 2024	Domain GeneralizationFederated Learning	—Unverified
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning	Jan 8, 2021	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Direct Federated Neural Architecture Search	Oct 13, 2020	Federated LearningNeural Architecture Search	—Unverified
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks	Dec 18, 2023	Federated LearningPersonalized Federated Learning	—Unverified
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks	Aug 14, 2023	Federated LearningMedical Image Analysis	—Unverified
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring	Jun 14, 2022	DiversityFederated Learning	—Unverified
Disentangling data distribution for Federated Learning	Oct 16, 2024	Federated Learning	—Unverified
DistDD: Distributed Data Distillation Aggregation through Gradient Matching	Oct 11, 2024	Federated LearningNeural Architecture Search	—Unverified
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data	Aug 14, 2020	Data AugmentationFederated Learning	—Unverified
Distilled One-Shot Federated Learning	Sep 17, 2020	Dataset DistillationFederated Learning	—Unverified
Distilling A Universal Expert from Clustered Federated Learning	Jun 25, 2025	Federated Learning	—Unverified
DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks	Jan 15, 2024	Federated LearningPrediction	—Unverified
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems	Dec 16, 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