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 351–375 of 6771 papers

Title	Date	Tasks	Status
LLMs meet Federated Learning for Scalable and Secure IoT Management	Apr 22, 2025	Computational EfficiencyDecision Making	—Unverified
TrojanDam: Detection-Free Backdoor Defense in Federated Learning through Proactive Model Robustification utilizing OOD Data	Apr 22, 2025	backdoor defenseFederated Learning	CodeCode Available
Towards a Distributed Federated Learning Aggregation Placement using Particle Swarm Intelligence	Apr 22, 2025	Federated Learning	—Unverified
Collaborative Split Federated Learning with Parallel Training and Aggregation	Apr 22, 2025	Federated Learning	—Unverified
Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach	Apr 21, 2025	Federated Learning	—Unverified
Bayesian Federated Learning for Continual Training	Apr 21, 2025	Continual LearningFederated Learning	—Unverified
FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching	Apr 21, 2025	Federated Learning	CodeCode Available
Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving	Apr 21, 2025	Federated LearningPrivacy Preserving	—Unverified
Efficient Federated Split Learning for Large Language Models over Communication Networks	Apr 20, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning	Apr 20, 2025	Federated Learning	—Unverified
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data	Apr 20, 2025	Federated Learning	—Unverified
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models	Apr 19, 2025	Domain AdaptationFederated Learning	—Unverified
Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Sampling	Apr 18, 2025	Federated Learning	—Unverified
SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework	Apr 18, 2025	Federated Learning	—Unverified
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning	Apr 17, 2025	Federated LearningModel Poisoning	—Unverified
VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture	Apr 17, 2025	Federated LearningLanguage Modeling	—Unverified
FedX: Adaptive Model Decomposition and Quantization for IoT Federated Learning	Apr 17, 2025	Federated LearningQuantization	—Unverified
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems	Apr 17, 2025	Federated LearningMeta-Learning	—Unverified
Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients	Apr 17, 2025	BenchmarkingFederated Learning	—Unverified
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum	Apr 17, 2025	Federated Learning	—Unverified
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs	Apr 16, 2025	Federated LearningFraud Detection	CodeCode Available
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning	Apr 16, 2025	Contrastive LearningDiversity	—Unverified
Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification	Apr 16, 2025	Clinical Concept ExtractionFederated Learning	—Unverified
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks	Apr 16, 2025	Computational EfficiencyFederated Learning	—Unverified
Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning	Apr 16, 2025	Federated LearningScheduling	—Unverified

Show:10 25 50

← PrevPage 15 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