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 3776–3800 of 6771 papers

Title	Date	Tasks	Status
Online Distributed Learning with Quantized Finite-Time Coordination	Jul 13, 2023	Federated Learning	—Unverified
Tackling Computational Heterogeneity in FL: A Few Theoretical Insights	Jul 12, 2023	Federated Learning	—Unverified
FDAPT: Federated Domain-adaptive Pre-training for Language Models	Jul 12, 2023	Computational EfficiencyFederated Learning	CodeCode Available
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators	Jul 11, 2023	Federated Learning	—Unverified
FedYolo: Augmenting Federated Learning with Pretrained Transformers	Jul 10, 2023	Federated Learning	—Unverified
DBFed: Debiasing Federated Learning Framework based on Domain-Independent	Jul 10, 2023	FairnessFederated Learning	—Unverified
Advances and Challenges in Meta-Learning: A Technical Review	Jul 10, 2023	Continual LearningDomain Adaptation	—Unverified
Fairness-aware Federated Minimax Optimization with Convergence Guarantee	Jul 10, 2023	FairnessFederated Learning	—Unverified
FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks	Jul 10, 2023	Federated LearningPrivacy Preserving	—Unverified
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles	Jul 10, 2023	Federated Learningparameter estimation	—Unverified
Federated Learning over a Wireless Network: Distributed User Selection through Random Access	Jul 7, 2023	FairnessFederated Learning	—Unverified
Federated Unlearning via Active Forgetting	Jul 7, 2023	Federated LearningIncremental Learning	—Unverified
Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem	Jul 7, 2023	FairnessFederated Learning	—Unverified
Meta Federated Reinforcement Learning for Distributed Resource Allocation	Jul 6, 2023	Federated LearningMeta-Learning	—Unverified
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence	Jul 6, 2023	Code GenerationFederated Learning	—Unverified
FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout	Jul 5, 2023	Federated LearningModel extraction	CodeCode Available
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives	Jul 5, 2023	Federated LearningSurvey	CodeCode Available
Personalized Federated Learning via Amortized Bayesian Meta-Learning	Jul 5, 2023	Federated LearningMeta-Learning	—Unverified
SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT	Jul 4, 2023	Federated LearningSelf-Supervised Learning	—Unverified
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems	Jul 4, 2023	Federated LearningLearning-To-Rank	—Unverified
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices	Jul 4, 2023	Federated LearningIndoor Localization	CodeCode Available
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks	Jul 4, 2023	Data PoisoningFederated Learning	—Unverified
Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions	Jul 3, 2023	Federated Learning	—Unverified
Defending Against Poisoning Attacks in Federated Learning with Blockchain	Jul 2, 2023	Federated Learning	—Unverified
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory	Jul 2, 2023	class-incremental learningClass Incremental 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