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 6226–6250 of 6771 papers

Title	Date	Tasks	Status
Personalized Federated Learning with Contextual Modulation and Meta-Learning	Dec 23, 2023	Federated LearningMeta-Learning	CodeCode Available
Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning	Sep 24, 2024	Federated Learning	CodeCode Available
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation	Apr 27, 2022	Federated Learning	CodeCode Available
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks	Mar 5, 2024	Data PoisoningFederated Learning	CodeCode Available
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation	Oct 29, 2023	Federated LearningInformation Retrieval	CodeCode Available
UnifyFL: Enabling Decentralized Cross-Silo Federated Learning	Apr 26, 2025	Federated Learning	CodeCode Available
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection	Jul 23, 2019	BIG-bench Machine LearningFederated Learning	CodeCode Available
Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment	Aug 22, 2023	Federated Learning	CodeCode Available
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning	Sep 8, 2022	Federated LearningPrivacy Preserving	CodeCode Available
Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation	Oct 4, 2024	Federated LearningSemantic Segmentation	CodeCode Available
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments	Sep 13, 2023	Autonomous VehiclesFederated Learning	CodeCode Available
Mitigating Adversarial Attacks on ECG Classification in Federated Learning via Adversarial Training	May 1, 2025	ECG ClassificationElectrocardiography (ECG)	CodeCode Available
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees	Feb 5, 2024	Federated Learning	CodeCode Available
Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation	Jun 20, 2023	Federated Learning	CodeCode Available
FLoCoRA: Federated learning compression with low-rank adaptation	Jun 20, 2024	Federated LearningModel Compression	CodeCode Available
Addressing Heterogeneity in Federated Learning via Distributional Transformation	Oct 26, 2022	Data AugmentationFederated Learning	CodeCode Available
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning	Apr 12, 2024	Federated LearningImage Captioning	CodeCode Available
Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home Environments	Mar 12, 2024	Continual LearningFederated Learning	CodeCode Available
Towards Hyper-parameter-free Federated Learning	Aug 30, 2024	Federated Learning	CodeCode Available
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning	Oct 28, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs	Jun 2, 2023	Federated LearningTime Series	CodeCode Available
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning	Feb 15, 2023	Federated Learning	CodeCode Available
Policy-Based Federated Learning	Mar 14, 2020	Federated Learningimage-classification	CodeCode Available
Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing	Nov 28, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available
FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients	May 17, 2025	backdoor defenseFederated Learning	CodeCode Available

Show:10 25 50

← PrevPage 250 of 271Next →

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