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 2601–2625 of 6771 papers

Title	Date	Tasks	Status
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends	Dec 19, 2022	Adversarial AttackBackdoor Attack	—Unverified
Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach	Jan 14, 2020	FairnessFederated Learning	—Unverified
Defending against Poisoning Backdoor Attacks on Federated Meta-learning	Sep 23, 2022	Federated LearningMeta-Learning	—Unverified
Defending Against Poisoning Attacks in Federated Learning with Blockchain	Jul 2, 2023	Federated Learning	—Unverified
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning	Jun 18, 2024	Data PoisoningFederated Learning	—Unverified
Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbation	Mar 19, 2025	Federated LearningPrivacy Preserving	—Unverified
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning	Jan 22, 2025	Federated LearningPersonalized Federated Learning	—Unverified
AirTOWN: A Privacy-Preserving Mobile App for Real-time Pollution-Aware POI Suggestion	Jan 23, 2025	Collaborative FilteringFederated Learning	—Unverified
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach	Mar 2, 2024	Federated Learning	—Unverified
BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks	Nov 1, 2024	FairnessFederated Learning	—Unverified
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory	Oct 25, 2023	Federated Learning	—Unverified
Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning	Jul 26, 2024	Federated LearningInference Attack	—Unverified
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning	Jul 16, 2025	Data PoisoningFederated Learning	—Unverified
Deep Unfolding-based Weighted Averaging for Federated Learning in Heterogeneous Environments	Dec 23, 2022	Federated Learning	—Unverified
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks	Dec 2, 2021	Federated LearningIntrusion Detection	—Unverified
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data	Sep 19, 2024	Computational EfficiencyFederated Learning	—Unverified
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection	Jan 3, 2022	Federated Learning	—Unverified
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers	Aug 8, 2023	Backdoor AttackFederated Learning	—Unverified
Data science and AI in FinTech: An overview	Jul 10, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing	Apr 6, 2023	Deep Reinforcement LearningEdge-computing	—Unverified
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets	Jun 23, 2022	Deep Reinforcement LearningDemand Forecasting	—Unverified
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection	Feb 21, 2022	backdoor defenseFederated Learning	—Unverified
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT	Feb 3, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings	May 17, 2022	Federated LearningSegmentation	—Unverified
Backdoor Attacks on Federated Meta-Learning	Jun 12, 2020	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