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 1251–1275 of 6771 papers

Title	Date	Tasks	Status
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation	Mar 29, 2025	FairnessFederated Learning	—Unverified
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning	Mar 29, 2025	DiversityFederated Learning	—Unverified
Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data	Mar 27, 2025	Federated LearningPrivacy Preserving	—Unverified
Federated Learning with Differential Privacy: An Utility-Enhanced Approach	Mar 27, 2025	Federated Learning	—Unverified
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distance	Mar 27, 2025	Computational EfficiencyFederated Learning	CodeCode Available
The Cost of Local and Global Fairness in Federated Learning	Mar 27, 2025	FairnessFederated Learning	CodeCode Available
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning	Mar 27, 2025	Federated LearningPrivacy Preserving	—Unverified
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning	Mar 27, 2025	Federated Learning	—Unverified
Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory	Mar 27, 2025	Decision MakingFederated Learning	—Unverified
ProFed: a Benchmark for Proximity-based non-IID Federated Learning	Mar 26, 2025	Federated Learning	—Unverified
MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation	Mar 26, 2025	Federated LearningImage Segmentation	—Unverified
An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy	Mar 26, 2025	Federated Learning	—Unverified
TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models	Mar 26, 2025	Federated Learningimage-classification	CodeCode Available
Convergence Theory of Flexible ALADIN for Distributed Optimization	Mar 26, 2025	Distributed OptimizationFederated Learning	—Unverified
Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation	Mar 26, 2025	Federated Learning	—Unverified
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions	Mar 26, 2025	Federated Learning	—Unverified
Robust Federated Learning Against Poisoning Attacks: A GAN-Based Defense Framework	Mar 26, 2025	Federated LearningGenerative Adversarial Network	CodeCode Available
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks	Mar 25, 2025	Anomaly DetectionComputational Efficiency	—Unverified
SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perception	Mar 25, 2025	Computational EfficiencyFederated Learning	—Unverified
Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable	Mar 25, 2025	Federated Learning	—Unverified
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data	Mar 25, 2025	Data IntegrationFederated Learning	—Unverified
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning	Mar 25, 2025	Continual LearningFederated Learning	CodeCode Available
Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks	Mar 25, 2025	Federated LearningPrivacy Preserving	—Unverified
RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized Federated Learning	Mar 25, 2025	ClusteringFederated Learning	—Unverified
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments	Mar 25, 2025	Federated LearningOut-of-Distribution Generalization	—Unverified

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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