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 2926–2950 of 6771 papers

Title	Date	Tasks	Status
SPEAR:Exact Gradient Inversion of Batches in Federated Learning	Mar 6, 2024	Federated LearningGPU	—Unverified
OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning	Mar 6, 2024	Federated Learning	—Unverified
Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem	Mar 6, 2024	Federated Learning	—Unverified
Many-Objective Multi-Solution Transport	Mar 6, 2024	Federated LearningMulti-Task Learning	—Unverified
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data	Mar 6, 2024	Federated LearningVertical Federated Learning	—Unverified
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation	Mar 5, 2024	Anomaly DetectionComputational Efficiency	—Unverified
Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks	Mar 5, 2024	Cloud ComputingDeep Reinforcement Learning	—Unverified
Training Machine Learning models at the Edge: A Survey	Mar 5, 2024	Edge-computingFederated Learning	—Unverified
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks	Mar 5, 2024	Data PoisoningFederated Learning	CodeCode Available
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks	Mar 5, 2024	Federated Learning	—Unverified
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning	Mar 5, 2024	Federated LearningMulti-agent Reinforcement Learning	CodeCode Available
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models	Mar 5, 2024	Contrastive LearningFederated Learning	CodeCode Available
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures	Mar 5, 2024	Federated LearningPrivacy Preserving	—Unverified
Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks	Mar 5, 2024	Federated Learning	—Unverified
Federated Learning over Connected Modes	Mar 5, 2024	ClusteringFederated Learning	CodeCode Available
Towards Robust Federated Learning via Logits Calibration on Non-IID Data	Mar 5, 2024	Federated LearningPrivacy Preserving	—Unverified
MeanCache: User-Centric Semantic Caching for LLM Web Services	Mar 5, 2024	Federated LearningLanguage Modeling	—Unverified
A Survey on Federated Unlearning: Challenges and Opportunities	Mar 4, 2024	Federated LearningMachine Unlearning	—Unverified
Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling	Mar 4, 2024	Federated Learning	CodeCode Available
Enhancing Data Provenance and Model Transparency in Federated Learning Systems - A Database Approach	Mar 3, 2024	Federated Learning	—Unverified
A Hierarchical Federated Learning Approach for the Internet of Things	Mar 3, 2024	Federated LearningQuantization	—Unverified
Partial Federated Learning	Mar 3, 2024	Contrastive LearningFederated Learning	—Unverified
Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation	Mar 3, 2024	Federated Learning	—Unverified
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications	Mar 3, 2024	Federated LearningSurvey	—Unverified
Analysis of Privacy Leakage in Federated Large Language Models	Mar 2, 2024	Federated Learning	CodeCode Available

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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