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 5151–5175 of 6771 papers

Title	Date	Tasks	Status
Adaptive Federated Pruning in Hierarchical Wireless Networks	May 15, 2023	Federated LearningPrivacy Preserving	—Unverified
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems	Nov 22, 2023	Federated Learning	—Unverified
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach	Jan 14, 2020	FairnessFederated Learning	—Unverified
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning	Dec 11, 2020	Federated Learning	—Unverified
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning	Jan 1, 2024	Federated LearningGraph Learning	—Unverified
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach	Feb 15, 2023	Federated LearningMulti-agent Reinforcement Learning	—Unverified
Adaptively Weighted Averaging Over-the-Air Computation and Its Application to Distributed Gaussian Process Regression	Jan 29, 2025	Federated LearningGPR	—Unverified
Adaptive Parameterization of Deep Learning Models for Federated Learning	Feb 6, 2023	Deep LearningFederated Learning	—Unverified
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data	Jul 7, 2022	ClusteringFederated Learning	—Unverified
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks	Feb 6, 2025	Federated LearningTransfer Learning	—Unverified
Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning	Feb 8, 2021	Federated LearningQuantization	—Unverified
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks	Dec 14, 2024	Federated LearningQuantization	—Unverified
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models	Jan 24, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
Adaptive Scheduling for Machine Learning Tasks over Networks	Jan 25, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning	Jun 19, 2025	Deep Reinforcement LearningFederated Learning	—Unverified
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning	Mar 2, 2021	Federated LearningScheduling	—Unverified
Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT Networks	Mar 8, 2025	Disaster ResponseFederated Learning	—Unverified
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks	Mar 19, 2024	Edge-computingFederated Learning	—Unverified
Adapt to Adaptation: Learning to Personalize for Cross-Silo Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning	Dec 2, 2021	Deep LearningFederated 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
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization	Mar 24, 2022	Continual LearningFederated Learning	—Unverified
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models	Jul 20, 2024	DiversityEpidemiology	—Unverified
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration	Oct 2, 2024	Federated LearningPersonalized Federated Learning	—Unverified
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment	Aug 18, 2024	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