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 1001–1025 of 6771 papers

Title	Date	Tasks	Status
An Optimization Framework for Federated Edge Learning	Nov 26, 2021	Edge-computingFederated Learning	—Unverified
Centroid Approximation for Byzantine-Tolerant Federated Learning	Jun 18, 2025	Distributed ComputingFederated Learning	—Unverified
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points	Jun 29, 2021	Federated Learningglobal-optimization	—Unverified
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing	Mar 30, 2021	Federated Learning	—Unverified
Certified Federated Adversarial Training	Dec 20, 2021	Adversarial RobustnessFederated Learning	—Unverified
Certified Robustness for Free in Differentially Private Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model	Oct 20, 2023	Federated Learning	—Unverified
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting	Oct 12, 2024	Federated Learning	—Unverified
CFLIT: Coexisting Federated Learning and Information Transfer	Jul 26, 2022	Federated Learning	—Unverified
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models	May 22, 2024	DecoderFederated Learning	—Unverified
An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy	Mar 26, 2025	Federated Learning	—Unverified
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images	Jan 26, 2022	Active LearningBIG-bench Machine Learning	—Unverified
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training	Apr 20, 2024	Federated Learning	—Unverified
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning	May 26, 2023	Federated LearningPrivacy Preserving	—Unverified
Addressing Spatial-Temporal Data Heterogeneity in Federated Continual Learning via Tail Anchor	Dec 24, 2024	Continual LearningFederated Learning	—Unverified
Characterization of the Global Bias Problem in Aerial Federated Learning	Dec 29, 2022	Federated Learning	—Unverified
Chisme: Fully Decentralized Differentiated Deep Learning for Edge Intelligence	May 14, 2025	Deep LearningFederated Learning	—Unverified
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning	Feb 23, 2024	Federated Learning	—Unverified
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX	May 4, 2021	Federated Learning	—Unverified
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization	Jun 20, 2024	Federated Learning	—Unverified
Breaking the centralized barrier for cross-device federated learning	Dec 1, 2021	Federated Learning	—Unverified
Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI	Mar 19, 2025	Deep LearningFederated Learning	—Unverified
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning	Jun 22, 2024	Federated LearningInference Attack	—Unverified
An Empirical Study of Federated Prompt Learning for Vision Language Model	May 29, 2025	Federated LearningLanguage Modeling	—Unverified
Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning	Jun 7, 2025	Continual LearningFederated Learning	—Unverified

Show:10 25 50

← PrevPage 41 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