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 776–800 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
ARFED: Attack-Resistant Federated averaging based on outlier elimination	Nov 8, 2021	Data PoisoningFederated Learning	CodeCode Available	1	5
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification	Jun 3, 2022	Deep LearningFederated Learning	CodeCode Available	1	5
Federated Cross Learning for Medical Image Segmentation	Apr 5, 2022	Ensemble LearningFederated Learning	CodeCode Available	1	5
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data	Nov 22, 2022	Data AugmentationFederated Learning	CodeCode Available	1	5
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks	Dec 7, 2021	Autonomous VehiclesDeep Reinforcement Learning	CodeCode Available	1	5
Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer	Oct 3, 2022	Domain GeneralizationFederated Learning	CodeCode Available	1	5
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better	Dec 18, 2021	Federated Learning	CodeCode Available	1	5
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations	Aug 21, 2023	Federated Learning	CodeCode Available	1	5
Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks	Jun 14, 2023	Federated LearningPrediction	CodeCode Available	1	5
Federated Few-shot Learning	Jun 17, 2023	ArticlesFederated Learning	CodeCode Available	1	5
FwdLLM: Efficient FedLLM using Forward Gradient	Aug 26, 2023	Federated Learning	CodeCode Available	1	5
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning	Jun 19, 2020	Federated LearningStochastic Optimization	CodeCode Available	1	5
Federated Foundation Models on Heterogeneous Time Series	Dec 12, 2024	Anomaly DetectionFederated Learning	CodeCode Available	1	5
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes	Dec 11, 2023	Federated LearningLarge Language Model	CodeCode Available	1	5
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent	Sep 11, 2020	Bayesian InferenceFederated Learning	CodeCode Available	1	5
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training	Oct 11, 2021	Federated LearningImage Segmentation	CodeCode Available	1	5
Projected Federated Averaging with Heterogeneous Differential Privacy	Dec 1, 2021	Federated Learning	CodeCode Available	1	5
Federated Incremental Semantic Segmentation	Apr 10, 2023	Federated LearningRelation	CodeCode Available	1	5
Federated Knowledge Distillation	Nov 4, 2020	Federated LearningKnowledge Distillation	CodeCode Available	1	5
Improving Generalization in Federated Learning by Seeking Flat Minima	Mar 22, 2022	Domain GeneralizationFederated Learning	CodeCode Available	1	5
Bayesian Framework for Gradient Leakage	Nov 8, 2021	Federated Learning	CodeCode Available	1	5
Federated Learning Based on Dynamic Regularization	Nov 8, 2021	Federated Learning	CodeCode Available	1	5
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance	Dec 21, 2023	Explainable ModelsFault localization	CodeCode Available	1	5
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images	Sep 21, 2020	Deep LearningDiagnostic	CodeCode Available	1	5
Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing	Feb 26, 2023	Autonomous VehiclesEdge-computing	CodeCode Available	1	5

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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