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 4651–4675 of 6771 papers

Title	Date	Tasks	Status
A Survey on Heterogeneous Federated Learning	Oct 10, 2022	Federated LearningSurvey	—Unverified
Collaborative Domain Blocking: Using federated NLP To Detect Malicious Domains	Oct 8, 2022	BlockingFederated Learning	—Unverified
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems	Oct 8, 2022	Adversarial AttackFederated Learning	—Unverified
Rethinking Normalization Methods in Federated Learning	Oct 7, 2022	Federated Learning	—Unverified
Time Minimization in Hierarchical Federated Learning	Oct 7, 2022	Federated Learning	—Unverified
Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent	Oct 7, 2022	Federated Learning	—Unverified
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning	Oct 7, 2022	BlockingFederated Learning	—Unverified
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings	Oct 7, 2022	Federated LearningText Generation	—Unverified
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data	Oct 6, 2022	Auxiliary LearningFederated Learning	—Unverified
Communication-Efficient and Drift-Robust Federated Learning via Elastic Net	Oct 6, 2022	Federated Learning	—Unverified
FedGraph: an Aggregation Method from Graph Perspective	Oct 6, 2022	Federated LearningTumor Segmentation	—Unverified
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing	Oct 6, 2022	Federated Learning	—Unverified
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations	Oct 5, 2022	Federated Learning	—Unverified
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling	Oct 5, 2022	DiversityFederated Learning	CodeCode Available
FedMT: Federated Learning with Mixed-type Labels	Oct 5, 2022	Federated LearningVocal Bursts Type Prediction	—Unverified
Decentralized Hyper-Gradient Computation over Time-Varying Directed Networks	Oct 5, 2022	Bilevel OptimizationFederated Learning	CodeCode Available
Invariant Aggregator for Defending against Federated Backdoor Attacks	Oct 4, 2022	Federated LearningModel Optimization	—Unverified
Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning	Oct 4, 2022	Federated LearningKnowledge Distillation	—Unverified
Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach	Oct 4, 2022	Federated LearningManagement	—Unverified
Group Personalized Federated Learning	Oct 4, 2022	Federated LearningPersonalized Federated Learning	—Unverified
Split Federated Learning on Micro-controllers: A Keyword Spotting Showcase	Oct 4, 2022	Federated LearningKeyword Spotting	—Unverified
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning	Oct 4, 2022	Federated Learning	—Unverified
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning	Oct 4, 2022	Federated Learningparameter-efficient fine-tuning	—Unverified
Federated Graph-based Networks with Shared Embedding	Oct 3, 2022	Federated Learning	—Unverified
Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests	Oct 3, 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