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 2951–2975 of 6771 papers

Title	Date	Tasks	Status
Federated Learning with Limited Node Labels	Jun 18, 2024	Federated LearningNode Classification	—Unverified
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication	Feb 9, 2021	Federated Learning	—Unverified
Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition	May 11, 2025	Federated Learning	—Unverified
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization	Apr 23, 2022	Federated LearningQuantization	—Unverified
Federated Learning with Manifold Regularization and Normalized Update Reaggregation	Nov 10, 2023	Federated Learning	—Unverified
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features	Aug 30, 2023	Federated LearningRepresentation Learning	—Unverified
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification	Oct 21, 2024	Federated LearningFew-Shot Learning	—Unverified
Federated Learning with Multi-resolution Model Broadcast	May 30, 2024	Federated Learningmodel	—Unverified
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization	Mar 1, 2025	Federated LearningScheduling	—Unverified
Federated Learning with Neural Graphical Models	Sep 20, 2023	Federated Learning	—Unverified
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization	Jan 19, 2022	Federated LearningPrivacy Preserving	—Unverified
Deep Equilibrium Models Meet Federated Learning	May 29, 2023	Federated Learning	—Unverified
Federated Learning with Noisy User Feedback	May 6, 2022	Federated Learningtext-classification	—Unverified
Deep Federated Anomaly Detection for Multivariate Time Series Data	May 9, 2022	Anomaly DetectionConstrained Clustering	—Unverified
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning	Jun 9, 2025	Federated LearningScheduling	—Unverified
Federated Learning with Nonvacuous Generalisation Bounds	Oct 17, 2023	Federated Learning	—Unverified
Heterogeneity-Aware Client Sampling: A Unified Solution for Consistent Federated Learning	May 16, 2025	Federated Learning	—Unverified
Federated Learning without Full Labels: A Survey	Mar 25, 2023	Federated LearningSelf-Supervised Learning	—Unverified
Federated Learning without Revealing the Decision Boundaries	Mar 1, 2021	Federated LearningPrivacy Preserving	—Unverified
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach	Dec 25, 2024	Federated LearningImage Segmentation	—Unverified
Federated Object Detection for Quality Inspection in Shared Production	Jun 30, 2023	Federated LearningObject	—Unverified
Federated Learning with Personalization Layers	Dec 2, 2019	BIG-bench Machine LearningFederated Learning	—Unverified
Federated Learning with Position-Aware Neurons	Mar 28, 2022	Federated LearningPosition	—Unverified
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing	Aug 31, 2023	Compressive SensingComputational Efficiency	—Unverified
Federated Neural Collaborative Filtering	Jun 2, 2021	Collaborative FilteringFederated 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