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 1101–1125 of 6771 papers

Title	Date	Tasks	Status
Blockchain-based Secure Client Selection in Federated Learning	May 11, 2022	Federated Learning	—Unverified
Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning	Jul 25, 2023	Federated Learning	—Unverified
Anarchic Federated Learning	Aug 23, 2021	Federated Learning	—Unverified
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning	Sep 30, 2022	Federated Learning	—Unverified
Blockchain-based Framework for Scalable and Incentivized Federated Learning	Feb 20, 2025	FairnessFederated Learning	—Unverified
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems	Jul 4, 2023	Federated LearningLearning-To-Rank	—Unverified
Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things	Apr 25, 2023	Federated Learning	—Unverified
Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles	Mar 1, 2021	Edge-computingFederated Learning	—Unverified
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks	Jul 4, 2023	Data PoisoningFederated Learning	—Unverified
Blockchain-based Federated Learning for Decentralized Energy Management Systems	Jun 23, 2023	energy managementenergy trading	—Unverified
Blockchain-based Federated Learning for Failure Detection in Industrial IoT	Sep 6, 2020	Federated LearningPrivacy Preserving	—Unverified
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario	Oct 20, 2022	Data PoisoningFederated Learning	—Unverified
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks	Nov 13, 2023	class-incremental learningClass Incremental Learning	—Unverified
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients	Feb 17, 2024	Federated Learning	—Unverified
A Systematic Survey of Blockchained Federated Learning	Oct 5, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior	Jul 19, 2023	FairnessFederated Learning	—Unverified
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing	Mar 19, 2024	Federated LearningModel Poisoning	—Unverified
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation	Jan 18, 2021	Federated Learning	—Unverified
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients	Dec 2, 2020	Federated Learning	—Unverified
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning	Dec 2, 2021	Deep LearningFederated Learning	—Unverified
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing	Oct 16, 2021	Federated LearningPrivacy Preserving	—Unverified
Label Inference Attacks against Node-level Vertical Federated GNNs	Aug 4, 2023	Federated LearningNode Classification	—Unverified
Analytic Personalized Federated Meta-Learning	Feb 10, 2025	Federated LearningMeta-Learning	—Unverified
Fabricated Flips: Poisoning Federated Learning without Data	Feb 7, 2022	Federated Learning	—Unverified
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	Jun 16, 2022	BIG-bench Machine LearningFederated 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