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 4601–4625 of 6771 papers

Title	Date	Tasks	Status
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges	Mar 10, 2025	Distributed OptimizationFederated Learning	—Unverified
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare	Sep 15, 2024	Federated Learning	—Unverified
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning	Nov 11, 2022	Federated LearningPneumonia Detection	—Unverified
From Distributed Machine Learning to Federated Learning: A Survey	Apr 29, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks	Jun 7, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks	Nov 2, 2024	Federated Learning	—Unverified
Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable	Mar 25, 2025	Federated Learning	—Unverified
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection	Mar 30, 2024	Federated Learning	—Unverified
From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling	May 22, 2025	Federated LearningStock Price Prediction	—Unverified
From Local SGD to Local Fixed-Point Methods for Federated Learning	Apr 3, 2020	Federated Learning	—Unverified
From Local SGD to Local Fixed Point Methods for Federated Learning	Jan 1, 2020	Federated Learning	—Unverified
From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning	Jan 6, 2025	Federated LearningInference Attack	—Unverified
Meta Matrix Factorization for Federated Rating Predictions	Oct 22, 2019	Collaborative FilteringFederated Learning	—Unverified
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation	Jun 19, 2023	Action RecognitionFederated Learning	—Unverified
FSL: Federated Supermask Learning	Sep 29, 2021	Federated Learning	—Unverified
FRL: Federated Rank Learning	Oct 8, 2021	Federated Learning	—Unverified
FS-Real: Towards Real-World Cross-Device Federated Learning	Mar 23, 2023	Federated Learning	—Unverified
FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understanding	Apr 13, 2025	Contrastive LearningFederated Learning	—Unverified
FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning	Aug 31, 2023	Backdoor AttackFederated Learning	—Unverified
Model Inversion Attack against Federated Unlearning	Feb 20, 2025	Federated Learningmodel	—Unverified
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning	Mar 6, 2022	Federated LearningGaussian Processes	—Unverified
Secure Embedding Aggregation for Federated Representation Learning	Jun 18, 2022	Federated LearningPrivacy Preserving	—Unverified
Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association	Mar 6, 2025	Distributed ComputingFederated Learning	—Unverified
Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning	Jun 7, 2025	Federated LearningIntrusion Detection	—Unverified
Fusion Learning: A One Shot Federated Learning	Jun 15, 2020	Federated Learning	—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