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 951–975 of 6771 papers

Title	Date	Tasks	Status	Hype
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning	Dec 16, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Modeling Inter-Intra Heterogeneity for Graph Federated Learning	Dec 16, 2024	Federated Learning	CodeCode Available	0
Information-Geometric Barycenters for Bayesian Federated Learning	Dec 16, 2024	Bayesian InferenceDistributed Optimization	—Unverified	0
Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning	Dec 16, 2024	Federated Learningimage-classification	—Unverified	0
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning	Dec 16, 2024	Federated LearningScheduling	—Unverified	0
UA-PDFL: A Personalized Approach for Decentralized Federated Learning	Dec 16, 2024	Federated LearningPrivacy Preserving	—Unverified	0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning	Dec 16, 2024	Federated LearningImage Generation	CodeCode Available	0
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training	Dec 16, 2024	Domain GeneralizationFederated Learning	—Unverified	0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes	Dec 15, 2024	Federated LearningKnowledge Distillation	—Unverified	0
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression	Dec 14, 2024	Computational EfficiencyDiversity	CodeCode Available	0
Predicting Survival of Hemodialysis Patients using Federated Learning	Dec 14, 2024	Federated Learning	—Unverified	0
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks	Dec 14, 2024	Federated LearningQuantization	—Unverified	0
Client-Side Patching against Backdoor Attacks in Federated Learning	Dec 13, 2024	Federated Learning	—Unverified	0
ExclaveFL: Providing Transparency to Federated Learning using Exclaves	Dec 13, 2024	Federated Learning	—Unverified	0
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series Data	Dec 13, 2024	Causal DiscoveryFederated Learning	CodeCode Available	0
Deep Learning Model Security: Threats and Defenses	Dec 12, 2024	Data PoisoningDeep Learning	—Unverified	0
Federated Foundation Models on Heterogeneous Time Series	Dec 12, 2024	Anomaly DetectionFederated Learning	CodeCode Available	1
Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learning	Dec 12, 2024	Federated Learning	—Unverified	0
Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation	Dec 11, 2024	BenchmarkingFederated Learning	CodeCode Available	0
dsLassoCov: a federated machine learning approach incorporating covariate control	Dec 11, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Federated In-Context LLM Agent Learning	Dec 11, 2024	Federated LearningIn-Context Learning	—Unverified	0
A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions	Dec 11, 2024	Federated LearningRecommendation Systems	—Unverified	0
How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?	Dec 11, 2024	Federated Learning	—Unverified	0
Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation	Dec 11, 2024	Data AugmentationFederated Learning	—Unverified	0
Learn How to Query from Unlabeled Data Streams in Federated Learning	Dec 11, 2024	BenchmarkingDecision Making	CodeCode Available	0

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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