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 4451–4475 of 6771 papers

Title	Date	Tasks	Status
FLAGS Framework for Comparative Analysis of Federated Learning Algorithms	Dec 14, 2022	Federated Learning	—Unverified
Robust Split Federated Learning for U-shaped Medical Image Networks	Dec 13, 2022	Federated Learning	CodeCode Available
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO	Dec 13, 2022	Federated Learning	—Unverified
AFLGuard: Byzantine-robust Asynchronous Federated Learning	Dec 13, 2022	Federated Learning	—Unverified
Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning	Dec 12, 2022	Federated LearningNatural Language Understanding	—Unverified
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals	Dec 11, 2022	Federated LearningQuantization	—Unverified
GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation Techniques	Dec 8, 2022	Federated Learning	CodeCode Available
Federated Learning for Inference at Anytime and Anywhere	Dec 8, 2022	Federated Learning	—Unverified
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy	Dec 8, 2022	Federated LearningMath	CodeCode Available
Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning	Dec 7, 2022	Fault DetectionFederated Learning	—Unverified
Reconstructing Training Data from Model Gradient, Provably	Dec 7, 2022	Federated Learningmodel	—Unverified
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles	Dec 7, 2022	Federated LearningTrajectory Prediction	—Unverified
PaDPaF: Partial Disentanglement with Partially-Federated GANs	Dec 7, 2022	DisentanglementFederated Learning	CodeCode Available
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning	Dec 5, 2022	Federated LearningSemantic Similarity	—Unverified
FedCC: Robust Federated Learning against Model Poisoning Attacks	Dec 5, 2022	Federated Learningmodel	—Unverified
Distributed Stochastic Gradient Descent with Cost-Sensitive and Strategic Agents	Dec 5, 2022	Federated Learning	—Unverified
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks	Dec 5, 2022	FairnessFederated Learning	—Unverified
Encrypted machine learning of molecular quantum properties	Dec 5, 2022	Federated Learningmolecular representation	CodeCode Available
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics	Dec 5, 2022	Federated LearningKnowledge Tracing	—Unverified
Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning	Dec 5, 2022	Federated Learning	—Unverified
Distributed Pruning Towards Tiny Neural Networks in Federated Learning	Dec 5, 2022	Federated LearningNetwork Pruning	—Unverified
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation	Dec 5, 2022	DiversityFederated Learning	—Unverified
Security Analysis of SplitFed Learning	Dec 4, 2022	Federated LearningModel Poisoning	—Unverified
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding	Dec 4, 2022	Federated Learningimage-classification	—Unverified
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning	Dec 3, 2022	Federated LearningModel Compression	—Unverified

Show:10 25 50

← PrevPage 179 of 271Next →

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