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 426–450 of 6771 papers

Title	Date	Tasks	Status	Hype
Efficient Federated Learning Tiny Language Models for Mobile Network Feature Prediction	Apr 2, 2025	Federated Learning	CodeCode Available	2
Informed Greedy Algorithm for Scalable Bayesian Network Fusion via Minimum Cut Analysis	Apr 1, 2025	Federated Learning	CodeCode Available	0
Global Intervention and Distillation for Federated Out-of-Distribution Generalization	Apr 1, 2025	AttributeData Augmentation	—Unverified	0
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization	Apr 1, 2025	Computational EfficiencyFederated Learning	—Unverified	0
Personalized Federated Training of Diffusion Models with Privacy Guarantees	Apr 1, 2025	Federated Learning	—Unverified	0
Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos	Apr 1, 2025	Federated LearningPersonalized Federated Learning	—Unverified	0
Benchmarking Federated Machine Unlearning methods for Tabular Data	Apr 1, 2025	BenchmarkingComputational Efficiency	—Unverified	0
EMO: Edge Model Overlays to Scale Model Size in Federated Learning	Apr 1, 2025	Federated Learningmodel	—Unverified	0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing	Mar 31, 2025	Brain Tumor SegmentationData Augmentation	—Unverified	0
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration	Mar 31, 2025	Federated Learning	—Unverified	0
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation	Mar 31, 2025	Federated Learning	—Unverified	0
Federated Structured Sparse PCA for Anomaly Detection in IoT Networks	Mar 31, 2025	Anomaly DetectionFederated Learning	—Unverified	0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection	Mar 30, 2025	DiagnosticFederated Learning	—Unverified	0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation	Mar 30, 2025	Federated LearningImage Segmentation	—Unverified	0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior	Mar 30, 2025	Data IntegrationFederated Learning	—Unverified	0
Two Heads Are Better than One: Model-Weight and Latent-Space Analysis for Federated Learning on Non-iid Data against Poisoning Attacks	Mar 30, 2025	Federated LearningModel Poisoning	—Unverified	0
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering	Mar 30, 2025	AllContrastive Learning	—Unverified	0
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning	Mar 29, 2025	DiversityFederated Learning	—Unverified	0
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation	Mar 29, 2025	FairnessFederated Learning	—Unverified	0
FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning	Mar 28, 2025	Federated LearningPrompt Learning	CodeCode Available	1
The Cost of Local and Global Fairness in Federated Learning	Mar 27, 2025	FairnessFederated Learning	CodeCode Available	0
Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data	Mar 27, 2025	Federated LearningPrivacy Preserving	—Unverified	0
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning	Mar 27, 2025	Federated Learning	—Unverified	0
Federated Learning with Differential Privacy: An Utility-Enhanced Approach	Mar 27, 2025	Federated Learning	—Unverified	0
Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory	Mar 27, 2025	Decision MakingFederated Learning	—Unverified	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