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 1201–1225 of 6771 papers

Title	Date	Tasks	Status	Score
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection	May 2, 2024	Autonomous DrivingFederated Learning	CodeCode Available	5
Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities	May 20, 2024	Computational EfficiencyFederated Learning	CodeCode Available	5
Federated Learning With Individualized Privacy Through Client Sampling	Jan 29, 2025	Federated Learning	CodeCode Available	5
Federated Learning with Only Positive Labels	Apr 21, 2020	Federated LearningMulti-class Classification	CodeCode Available	5
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis	Apr 26, 2023	ClusteringFault Diagnosis	CodeCode Available	5
Federated Optimization for Heterogeneous Networks	May 16, 2019	Federated LearningMulti-Task Learning	CodeCode Available	5
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs	Aug 16, 2019	Federated Learning	CodeCode Available	5
Federated Learning via Plurality Vote	Oct 6, 2021	Federated LearningQuantization	CodeCode Available	5
Federated Learning with a Single Shared Image	Jun 18, 2024	Federated LearningKnowledge Distillation	CodeCode Available	5
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence	Nov 21, 2023	FairnessFederated Learning	CodeCode Available	5
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis	Oct 30, 2024	Federated Learning	CodeCode Available	5
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning	Nov 26, 2024	Activity RecognitionFederated Learning	CodeCode Available	5
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks	Mar 5, 2024	Data PoisoningFederated Learning	CodeCode Available	5
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection	Jul 23, 2019	BIG-bench Machine LearningFederated Learning	CodeCode Available	5
A Framework for testing Federated Learning algorithms using an edge-like environment	Jul 17, 2024	Edge-computingFederated Learning	CodeCode Available	5
Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home Environments	Mar 12, 2024	Continual LearningFederated Learning	CodeCode Available	5
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback	Dec 28, 2021	Federated LearningStochastic Optimization	CodeCode Available	5
Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment	Aug 22, 2023	Federated Learning	CodeCode Available	5
Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping	May 29, 2024	Federated Learning	CodeCode Available	5
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework	Jan 1, 2023	Federated Learning	CodeCode Available	5
Asynchronous Federated Optimization	Mar 10, 2019	Federated Learning	CodeCode Available	5
Federated Causal Inference from Observational Data	Aug 24, 2023	Causal InferenceFederated Learning	CodeCode Available	5
Federated Learning Meets Fairness and Differential Privacy	Aug 23, 2021	FairnessFederated Learning	CodeCode Available	5
Federated Learning of Large Models at the Edge via Principal Sub-Model Training	Aug 28, 2022	Federated Learning	CodeCode Available	5
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning	Sep 8, 2022	Federated LearningPrivacy Preserving	CodeCode Available	5

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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