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 3551–3600 of 6771 papers

Title	Date	Tasks	Status	Hype
SplitAMC: Split Learning for Robust Automatic Modulation Classification	Apr 17, 2023	ClassificationFederated Learning	—Unverified	0
FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigation	Apr 17, 2023	Federated Learning	—Unverified	0
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives	Apr 17, 2023	Federated LearningLoad Forecasting	—Unverified	0
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models	Apr 17, 2023	Federated LearningImputation	—Unverified	0
Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation	Apr 17, 2023	Federated Learning	—Unverified	0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification	Apr 16, 2023	Federated Learningimage-classification	—Unverified	0
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain	Apr 16, 2023	Computational EfficiencyFederated Learning	—Unverified	0
Practical Differentially Private and Byzantine-resilient Federated Learning	Apr 15, 2023	Federated LearningPrivacy Preserving	CodeCode Available	0
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training	Apr 15, 2023	Federated Learning	—Unverified	0
Data, Competition, and Digital Platforms	Apr 15, 2023	Federated Learning	—Unverified	0
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy	Apr 15, 2023	Federated Learning	—Unverified	0
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data	Apr 15, 2023	Federated LearningImage Reconstruction	CodeCode Available	0
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates	Apr 15, 2023	Federated Learning	—Unverified	0
IP-FL: Incentivized and Personalized Federated Learning	Apr 15, 2023	ClusteringFederated Learning	—Unverified	0
Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Tasks	Apr 14, 2023	FairnessFederated Learning	—Unverified	0
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review	Apr 14, 2023	ArticlesDistributed Computing	—Unverified	0
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition	Apr 14, 2023	Action RecognitionContinual Learning	—Unverified	0
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training	Apr 14, 2023	Federated Learning	—Unverified	0
Scale Federated Learning for Label Set Mismatch in Medical Image Classification	Apr 14, 2023	Federated Learningimage-classification	CodeCode Available	0
Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks	Apr 13, 2023	Federated Learning	—Unverified	0
Edge-cloud Collaborative Learning with Federated and Centralized Features	Apr 12, 2023	Edge-computingFederated Learning	—Unverified	0
Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation	Apr 12, 2023	channel selectionFederated Learning	CodeCode Available	2
TinyReptile: TinyML with Federated Meta-Learning	Apr 11, 2023	Federated LearningMeta-Learning	—Unverified	0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense	Apr 11, 2023	Adversarial AttackAttribute	—Unverified	0
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model	Apr 11, 2023	Federated Learning	—Unverified	0
HPN: Personalized Federated Hyperparameter Optimization	Apr 11, 2023	Federated LearningHyperparameter Optimization	—Unverified	0
A Game-theoretic Framework for Privacy-preserving Federated Learning	Apr 11, 2023	Federated LearningPrivacy Preserving	—Unverified	0
GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery	Apr 11, 2023	Drug DiscoveryFederated Learning	—Unverified	0
Federated Learning with Classifier Shift for Class Imbalance	Apr 11, 2023	Federated Learning	—Unverified	0
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning	Apr 11, 2023	Federated Learning	—Unverified	0
Balancing Privacy and Performance for Private Federated Learning Algorithms	Apr 11, 2023	Federated Learning	—Unverified	0
Over-the-Air Federated Learning Over MIMO Channels: A Sparse-Coded Multiplexing Approach	Apr 10, 2023	Federated Learning	—Unverified	0
Accelerating Hybrid Federated Learning Convergence under Partial Participation	Apr 10, 2023	Federated Learning	—Unverified	0
Probably Approximately Correct Federated Learning	Apr 10, 2023	Federated LearningPAC learning	—Unverified	0
Federated Incremental Semantic Segmentation	Apr 10, 2023	Federated LearningRelation	CodeCode Available	1
FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybrid	Apr 9, 2023	ClusteringFederated Learning	—Unverified	0
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy	Apr 9, 2023	Federated LearningScheduling	—Unverified	0
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning	Apr 9, 2023	Federated LearningVocal Bursts Valence Prediction	—Unverified	0
Asynchronous Federated Continual Learning	Apr 7, 2023	Continual LearningFederated Learning	CodeCode Available	1
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination	Apr 7, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients	Apr 6, 2023	Federated Learning	—Unverified	0
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing	Apr 6, 2023	Deep Reinforcement LearningEdge-computing	—Unverified	0
IoT Federated Blockchain Learning at the Edge	Apr 6, 2023	Federated Learning	—Unverified	0
Model-Driven Quantum Federated Learning (QFL)	Apr 5, 2023	Federated Learningmodel	—Unverified	0
FedBot: Enhancing Privacy in Chatbots with Federated Learning	Apr 4, 2023	ChatbotFederated Learning	—Unverified	0
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher	Apr 4, 2023	Federated LearningKnowledge Distillation	CodeCode Available	1
Online Learning with Adversaries: A Differential-Inclusion Analysis	Apr 4, 2023	Federated Learning	—Unverified	0
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation	Apr 4, 2023	Federated Learning	—Unverified	0
A Survey on Vertical Federated Learning: From a Layered Perspective	Apr 4, 2023	Federated LearningPrivacy Preserving	—Unverified	0
SLPerf: a Unified Framework for Benchmarking Split Learning	Apr 4, 2023	BenchmarkingDiversity	CodeCode Available	1

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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