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 5076–5100 of 6771 papers

Title	Date	Tasks	Status
Accessible Gesture-Driven Augmented Reality Interaction System	Jun 18, 2025	Federated LearningGesture Recognition	—Unverified
Accuracy and Privacy Evaluations of Collaborative Data Analysis	Jan 27, 2021	Dimensionality ReductionFederated Learning	—Unverified
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning	Jul 26, 2024	Federated LearningInference Attack	—Unverified
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits	Dec 6, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping	Dec 9, 2020	Federated Learning	—Unverified
Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)	Nov 8, 2023	DiagnosticFederated Learning	—Unverified
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning	May 31, 2024	Federated LearningModel Poisoning	—Unverified
A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets	Feb 15, 2024	Deep LearningFederated Learning	—Unverified
Achieving Fairness Across Local and Global Models in Federated Learning	Jun 24, 2024	FairnessFederated Learning	—Unverified
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization	Aug 23, 2022	FairnessFederated Learning	—Unverified
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients	Feb 14, 2021	Federated Learning	—Unverified
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients	Feb 17, 2024	Federated Learning	—Unverified
Achieving Linear Speedup in Non-IID Federated Bilevel Learning	Feb 10, 2023	Bilevel OptimizationFederated Learning	—Unverified
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning	Jan 27, 2021	Federated LearningOpen-Ended Question Answering	—Unverified
Achieving Personalized Federated Learning with Sparse Local Models	Jan 27, 2022	Federated LearningPersonalized Federated Learning	—Unverified
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions	Dec 12, 2020	Federated Learning	—Unverified
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points	Jun 29, 2021	Federated Learningglobal-optimization	—Unverified
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning	Apr 17, 2025	Federated LearningModel Poisoning	—Unverified
A Closer Look at Personalization in Federated Image Classification	Apr 22, 2022	ClassificationEdge-computing	—Unverified
A Coalition Formation Game Approach for Personalized Federated Learning	Feb 5, 2022	DiversityFederated Learning	—Unverified
A collaborative ensemble construction method for federated random forest	Jul 27, 2024	Federated Learning	—Unverified
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization	Oct 18, 2024	Bilevel OptimizationFederated Learning	—Unverified
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret	Jan 21, 2023	Federated LearningStochastic Optimization	—Unverified
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning	Dec 2, 2021	Federated Learning	—Unverified
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data	Sep 3, 2023	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