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 2326–2350 of 6771 papers

Title	Date	Tasks	Status
Distributed sequential federated learning	Jan 31, 2023	Distributed ComputingFederated Learning	—Unverified
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching	Oct 20, 2021	Edge-computingFederated Learning	—Unverified
Distributed Quasi-Newton Method for Fair and Fast Federated Learning	Jan 18, 2025	FairnessFederated Learning	—Unverified
Binary Federated Learning with Client-Level Differential Privacy	Aug 7, 2023	Federated LearningPrivacy Preserving	—Unverified
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression	Nov 25, 2022	Federated Learning	—Unverified
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks	Mar 19, 2024	Edge-computingFederated Learning	—Unverified
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization	Aug 23, 2022	FairnessFederated Learning	—Unverified
Distributed Optimization over Block-Cyclic Data	Feb 18, 2020	Distributed OptimizationFederated Learning	—Unverified
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence	Apr 2, 2023	Distributed OptimizationFederated Learning	—Unverified
BICompFL: Stochastic Federated Learning with Bi-Directional Compression	Jan 31, 2025	Federated Learning	—Unverified
Distributed Online Optimization with Stochastic Agent Availability	Nov 25, 2024	Federated Learning	—Unverified
Distributed Online Learning with Multiple Kernels	Feb 25, 2021	Federated LearningTime Series	—Unverified
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning	Feb 5, 2021	Distributed OptimizationFederated Learning	—Unverified
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity	Aug 4, 2023	Federated LearningScheduling	—Unverified
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation	Jun 7, 2022	Federated LearningVocal Bursts Type Prediction	—Unverified
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees	Oct 7, 2021	Distributed ComputingFederated Learning	—Unverified
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications	Dec 2, 2020	BIG-bench Machine LearningCloud Computing	—Unverified
Bias-Eliminating Augmentation Learning for Debiased Federated Learning	Jan 1, 2023	Federated Learningimage-classification	—Unverified
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data	Dec 10, 2020	Edge-computingFederated Learning	—Unverified
Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks	Mar 8, 2025	Disaster ResponseFederated Learning	—Unverified
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics	Jan 3, 2023	Federated LearningMulti-agent Reinforcement Learning	—Unverified
Distributed Machine Learning and the Semblance of Trust	Dec 21, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Biased Over-the-Air Federated Learning under Wireless Heterogeneity	Mar 28, 2024	Federated Learning	—Unverified
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network	Nov 10, 2020	Federated Learning	—Unverified
Distributed learning optimisation of Cox models can leak patient data: Risks and solutions	Apr 12, 2022	Federated 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