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 751–775 of 6771 papers

Title	Date	Tasks	Status	Hype
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning	Aug 23, 2021	Federated LearningMisconceptions	CodeCode Available	1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network	Jun 17, 2023	Backdoor AttackFederated Learning	CodeCode Available	1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning	Oct 27, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning	Jun 5, 2023	DecoderFederated Learning	CodeCode Available	1
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning	Oct 6, 2022	Federated Learning	CodeCode Available	1
Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning	May 28, 2025	Federated Learning	CodeCode Available	1
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging	Jun 8, 2022	Computed Tomography (CT)CT Reconstruction	CodeCode Available	1
IBM Federated Learning: an Enterprise Framework White Paper V0.1	Jul 22, 2020	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning	May 12, 2021	ClusteringFederated Learning	CodeCode Available	1
Improving Fairness via Federated Learning	Oct 29, 2021	FairnessFederated Learning	CodeCode Available	1
Improving Transferability of Network Intrusion Detection in a Federated Learning Setup	Jan 7, 2024	Federated LearningIntrusion Detection	CodeCode Available	1
FL-Market: Trading Private Models in Federated Learning	Jun 8, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Inverting Gradients -- How easy is it to break privacy in federated learning?	Mar 31, 2020	Federated Learning	CodeCode Available	1
Inverting Gradients - How easy is it to break privacy in federated learning?	Dec 1, 2020	Federated Learning	CodeCode Available	1
Joint Privacy Enhancement and Quantization in Federated Learning	Aug 23, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1
Knowledge-Aware Federated Active Learning with Non-IID Data	Nov 24, 2022	Active LearningFederated Learning	CodeCode Available	1
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks	Jun 15, 2021	Federated Learning	CodeCode Available	1
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning	Oct 5, 2022	Autonomous DrivingDomain Adaptation	CodeCode Available	1
Collaboration Equilibrium in Federated Learning	Aug 18, 2021	Federated Learning	CodeCode Available	1
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning	Oct 19, 2022	Federated Learning	CodeCode Available	1
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning	Nov 28, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Locally Adaptive Federated Learning	Jul 12, 2023	Federated Learning	CodeCode Available	1
Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning	Oct 31, 2024	Federated Learning	CodeCode Available	1
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training	Sep 21, 2023	backdoor defenseDistributed Computing	CodeCode Available	1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach	Feb 24, 2021	Federated LearningGenerative Adversarial Network	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