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 3501–3525 of 6771 papers

Title	Date	Tasks	Status
Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape	Jul 5, 2024	Federated LearningPrivacy Preserving	—Unverified
Non-Federated Multi-Task Split Learning for Heterogeneous Sources	May 31, 2024	Federated Learningimage-classification	—Unverified
Non-IID data and Continual Learning processes in Federated Learning: A long road ahead	Nov 26, 2021	BIG-bench Machine LearningContinual Learning	—Unverified
Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions	Nov 19, 2024	Federated LearningPrivacy Preserving	—Unverified
Hypernetwork-Driven Model Fusion for Federated Domain Generalization	Feb 10, 2024	Domain GeneralizationFederated Learning	—Unverified
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices	Feb 16, 2022	Federated LearningKnowledge Distillation	—Unverified
No Peek: A Survey of private distributed deep learning	Dec 8, 2018	Deep LearningFederated Learning	—Unverified
No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation	Dec 10, 2023	FairnessFederated Learning	—Unverified
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning	Mar 12, 2025	AllFederated Learning	—Unverified
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study	Mar 26, 2024	AllFederated Learning	—Unverified
NoT: Federated Unlearning via Weight Negation	Mar 7, 2025	Computational EfficiencyFederated Learning	—Unverified
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning	Jun 3, 2024	Federated LearningModel Poisoning	—Unverified
Now It Sounds Like You: Learning Personalized Vocabulary On Device	May 5, 2023	Federated LearningLanguage Modeling	—Unverified
NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel	Oct 2, 2024	Federated Learning	—Unverified
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning	Nov 23, 2023	Federated LearningImage Augmentation	—Unverified
OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning	Mar 6, 2024	Federated Learning	—Unverified
OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning	May 3, 2021	DisentanglementFederated Learning	—Unverified
OFDMA-F^2L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface	Nov 25, 2023	Federated Learning	—Unverified
Tighter Regret Analysis and Optimization of Online Federated Learning	May 13, 2022	Federated LearningQuantization	—Unverified
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement	Oct 9, 2024	Federated Learning	—Unverified
Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network	Dec 2, 2022	Federated Learning	—Unverified
On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator	Aug 5, 2021	Autonomous VehiclesFederated Learning	—Unverified
On-Board Federated Learning for Dense LEO Constellations	Nov 24, 2021	Earth ObservationFederated Learning	—Unverified
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond	Jun 10, 2022	Federated Learning	—Unverified
On Data Efficiency of Meta-learning	Jan 30, 2021	Federated LearningMeta-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