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 1226–1250 of 6771 papers

Title	Date	Tasks	Status	Score
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs	Aug 16, 2019	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
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence	Nov 21, 2023	FairnessFederated Learning	CodeCode Available	5
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning	Dec 23, 2024	Federated LearningPrivacy Preserving	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
Federated Learning with a Single Shared Image	Jun 18, 2024	Federated LearningKnowledge Distillation	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
Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment	Aug 22, 2023	Federated Learning	CodeCode Available	5
Federated Learning: Challenges, Methods, and Future Directions	Aug 21, 2019	BIG-bench Machine LearningDistributed Optimization	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
Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping	May 29, 2024	Federated Learning	CodeCode Available	5
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design	May 10, 2021	Federated Learning	CodeCode Available	5
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation	May 27, 2022	Federated Learning	CodeCode Available	5
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning	Oct 9, 2024	Federated Learning	CodeCode Available	5
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis	Dec 27, 2024	DiagnosticFederated 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
Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks	Dec 9, 2020	BIG-bench Machine LearningFederated Learning	CodeCode Available	5
Accelerating Federated Learning with a Global Biased Optimiser	Aug 20, 2021	Federated Learning	CodeCode Available	5
Federated Learning Meets Fairness and Differential Privacy	Aug 23, 2021	FairnessFederated Learning	CodeCode Available	5
A Field Guide to Federated Optimization	Jul 14, 2021	Federated Learning	CodeCode Available	5
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data	Jun 1, 2022	Federated Learningimage-classification	CodeCode Available	5
Federated Causal Inference from Observational Data	Aug 24, 2023	Causal InferenceFederated Learning	CodeCode Available	5
Federated Learning of Medical Concepts Embedding using BEHRT	May 22, 2023	Federated LearningLanguage Modeling	CodeCode Available	5
Federated Learning Hyper-Parameter Tuning from a System Perspective	Nov 24, 2022	Federated Learning	CodeCode Available	5
Federated Learning in ASR: Not as Easy as You Think	Sep 30, 2021	Federated Learningspeech-recognition	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