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 4751–4800 of 6771 papers

Title	Date	Tasks	Status
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction	Aug 30, 2022	Click-Through Rate PredictionFederated Learning	—Unverified
Reducing Impacts of System Heterogeneity in Federated Learning using Weight Update Magnitudes	Aug 30, 2022	Federated Learning	—Unverified
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces	Aug 29, 2022	AttributeFederated Learning	—Unverified
Effectiveness of Federated Learning and CNN Ensemble Architectures for Identifying Brain Tumors Using MRI Images	Aug 28, 2022	Federated Learning	CodeCode Available
Federated Learning of Large Models at the Edge via Principal Sub-Model Training	Aug 28, 2022	Federated Learning	CodeCode Available
Network-Level Adversaries in Federated Learning	Aug 27, 2022	Federated Learning	CodeCode Available
BOBA: Byzantine-Robust Federated Learning with Label Skewness	Aug 27, 2022	Federated LearningSelection bias	CodeCode Available
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge	Aug 27, 2022	Federated LearningModel Compression	CodeCode Available
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges	Aug 27, 2022	Federated LearningPrivacy Preserving	—Unverified
Tensor Decomposition based Personalized Federated Learning	Aug 27, 2022	DiversityFederated Learning	—Unverified
Abnormal Local Clustering in Federated Learning	Aug 26, 2022	ClusteringFederated Learning	—Unverified
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits	Aug 26, 2022	Federated LearningPrivacy Preserving	—Unverified
On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations	Aug 25, 2022	Federated Learning	—Unverified
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning	Aug 25, 2022	Backdoor AttackData Poisoning	—Unverified
PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model	Aug 24, 2022	Federated Learning	—Unverified
Towards Sparsified Federated Neuroimaging Models via Weight Pruning	Aug 24, 2022	Federated Learning	—Unverified
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis	Aug 24, 2022	Contrastive LearningFederated Learning	—Unverified
FedMCSA: Personalized Federated Learning via Model Components Self-Attention	Aug 23, 2022	Federated LearningPersonalized Federated Learning	—Unverified
Exact Penalty Method for Federated Learning	Aug 23, 2022	Federated Learning	CodeCode Available
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization	Aug 23, 2022	FairnessFederated Learning	—Unverified
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation	Aug 23, 2022	FairnessFederated Learning	—Unverified
Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals	Aug 23, 2022	Decision MakingElectrocardiography (ECG)	—Unverified
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering	Aug 22, 2022	ClusteringFederated Learning	—Unverified
FedOS: using open-set learning to stabilize training in federated learning	Aug 22, 2022	Federated LearningOpen Set Learning	CodeCode Available
Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images	Aug 21, 2022	Federated LearningPrivacy Preserving	—Unverified
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning	Aug 21, 2022	Federated Learningimage-classification	—Unverified
Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network	Aug 21, 2022	Federated LearningPrivacy Preserving	—Unverified
A Review of Federated Learning in Energy Systems	Aug 20, 2022	Federated Learning	—Unverified
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning	Aug 19, 2022	Federated LearningPrivacy Preserving	—Unverified
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation	Aug 19, 2022	AttributeClustering	—Unverified
Federated Learning of Neural ODE Models with Different Iteration Counts	Aug 19, 2022	Federated Learning	—Unverified
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels	Aug 19, 2022	Federated LearningLearning with noisy labels	CodeCode Available
Almost Cost-Free Communication in Federated Best Arm Identification	Aug 19, 2022	Federated Learning	—Unverified
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data	Aug 17, 2022	Federated LearningOpen-Ended Question Answering	—Unverified
FedPerm: Private and Robust Federated Learning by Parameter Permutation	Aug 16, 2022	Federated LearningInformation Retrieval	—Unverified
Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion	Aug 16, 2022	Federated LearningKnowledge Distillation	CodeCode Available
Knowledge-Injected Federated Learning	Aug 16, 2022	Federated Learning	CodeCode Available
FedMR: Fedreated Learning via Model Recombination	Aug 16, 2022	Federated Learningmodel	—Unverified
Federated Quantum Natural Gradient Descent for Quantum Federated Learning	Aug 15, 2022	Federated Learning	—Unverified
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation	Aug 15, 2022	Federated Learning	—Unverified
Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning	Aug 14, 2022	AllFederated Learning	CodeCode Available
Trustworthy Federated Learning via Blockchain	Aug 13, 2022	Autonomous DrivingDeep Reinforcement Learning	—Unverified
Dropout is NOT All You Need to Prevent Gradient Leakage	Aug 12, 2022	AllFederated Learning	CodeCode Available
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives	Aug 12, 2022	Federated LearningPersonalized Federated Learning	—Unverified
A Fast Blockchain-based Federated Learning Framework with Compressed Communications	Aug 12, 2022	Federated Learning	—Unverified
A Knowledge Distillation-Based Backdoor Attack in Federated Learning	Aug 12, 2022	Backdoor AttackFederated Learning	—Unverified
Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone	Aug 11, 2022	Federated Learning	—Unverified
A Modified UDP for Federated Learning Packet Transmissions	Aug 11, 2022	Federated Learning	—Unverified
Fast Heterogeneous Federated Learning with Hybrid Client Selection	Aug 10, 2022	ClusteringFederated Learning	—Unverified
Combining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study	Aug 9, 2022	Federated LearningImage Classification	—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