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 6651–6675 of 6771 papers

Title	Date	Tasks	Status
Fairness and Privacy Guarantees in Federated Contextual Bandits	Feb 5, 2024	FairnessFederated Learning	—Unverified
Fairness-aware Agnostic Federated Learning	Oct 10, 2020	FairnessFederated Learning	—Unverified
Fairness-Aware Client Selection for Federated Learning	Jul 20, 2023	FairnessFederated Learning	—Unverified
Fairness-aware Federated Learning	Sep 29, 2021	FairnessFederated Learning	—Unverified
Fairness-Aware Job Scheduling for Multi-Job Federated Learning	Jan 5, 2024	FairnessFederated Learning	—Unverified
Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks	Mar 14, 2024	FairnessFederated Learning	—Unverified
Fairness in Federated Learning: Fairness for Whom?	May 27, 2025	FairnessFederated Learning	—Unverified
Fairness in Federated Learning for Spatial-Temporal Applications	Jan 17, 2022	FairnessFederated Learning	—Unverified
Fairness in Federated Learning via Core-Stability	Nov 3, 2022	Decision MakingFairness	—Unverified
Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System	Nov 11, 2021	FairnessFederated Learning	—Unverified
Fairness Without Demographics in Human-Centered Federated Learning	Apr 30, 2024	FairnessFederated Learning	—Unverified
Fair Resource Allocation in Federated Learning	May 16, 2019	FairnessFederated Learning	—Unverified
Faithful Edge Federated Learning: Scalability and Privacy	Jun 30, 2021	Federated Learning	—Unverified
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation	Dec 10, 2023	Federated LearningKnowledge Distillation	—Unverified
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning	Mar 10, 2024	Federated Learning	—Unverified
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning	Nov 2, 2024	Edge-computingFederated Learning	—Unverified
FAMAC: A Federated Assisted Modified Actor-Critic Framework for Secured Energy Saving in 5G and Beyond Networks	Nov 24, 2023	Federated Learning	—Unverified
FAM: fast adaptive federated meta-learning	Aug 26, 2023	Federated LearningMeta-Learning	—Unverified
FaRO 2: an Open Source, Configurable Smart City Framework for Real-Time Distributed Vision and Biometric Systems	Sep 26, 2022	Federated LearningSelf-Driving Cars	—Unverified
Fast-adapting and Privacy-preserving Federated Recommender System	Apr 2, 2021	Federated LearningMeta-Learning	—Unverified
Fast Composite Optimization and Statistical Recovery in Federated Learning	Jul 17, 2022	Federated Learning	—Unverified
Fast Convergence Algorithm for Analog Federated Learning	Oct 30, 2020	Federated Learning	—Unverified
FedAgg: Adaptive Federated Learning with Aggregated Gradients	Mar 28, 2023	Federated Learning	—Unverified
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving	Sep 29, 2024	Autonomous DrivingFederated Learning	—Unverified
Fast-Convergent Federated Learning	Jul 26, 2020	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