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 6726–6750 of 6771 papers

Title	Date	Tasks	Status
FedCert: Federated Accuracy Certification	Oct 4, 2024	Federated Learning	CodeCode Available
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos	Feb 29, 2024	DiversityFederated Learning	CodeCode Available
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback	Dec 28, 2021	Federated LearningStochastic Optimization	CodeCode Available
Robust Heterogeneous Federated Learning under Data Corruption	Jan 1, 2023	Data AugmentationFederated Learning	CodeCode Available
IPLS : A Framework for Decentralized Federated Learning	Jan 6, 2021	Federated Learning	CodeCode Available
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV	Aug 17, 2024	CPUDeep Reinforcement Learning	CodeCode Available
IronForge: An Open, Secure, Fair, Decentralized Federated Learning	Jan 7, 2023	FairnessFederated Learning	CodeCode Available
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series Data	Dec 13, 2024	Causal DiscoveryFederated Learning	CodeCode Available
On Vessel Location Forecasting and the Effect of Federated Learning	May 30, 2024	Federated Learning	CodeCode Available
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling	Oct 5, 2022	DiversityFederated Learning	CodeCode Available
Is Non-IID Data a Threat in Federated Online Learning to Rank?	Apr 20, 2022	Federated LearningInformation Retrieval	CodeCode Available
DRIVE: One-bit Distributed Mean Estimation	May 18, 2021	Federated Learning	CodeCode Available
DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries	May 27, 2025	Federated LearningPrivacy Preserving	CodeCode Available
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks	Feb 1, 2025	Federated LearningKnowledge Distillation	CodeCode Available
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder	Nov 21, 2022	DecoderFederated Learning	CodeCode Available
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation	Jul 14, 2025	Domain AdaptationFederated Learning	CodeCode Available
Adaptive Compression in Federated Learning via Side Information	Jun 22, 2023	Federated Learning	CodeCode Available
Federated Learning over Connected Modes	Mar 5, 2024	ClusteringFederated Learning	CodeCode Available
FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition	Jul 14, 2020	Federated Learning	CodeCode Available
Open-Vocabulary Federated Learning with Multimodal Prototyping	Apr 1, 2024	Federated Learning	CodeCode Available
FDA-Opt: Communication-Efficient Federated Fine-Tuning of Language Models	May 7, 2025	Federated Learning	CodeCode Available
DNN gradient lossless compression: Can GenNorm be the answer?	Nov 15, 2021	Federated Learning	CodeCode Available
Divergence-aware Federated Self-Supervised Learning	Apr 9, 2022	Federated LearningFederated Unsupervised Learning	CodeCode Available
Solving a Class of Non-Convex Minimax Optimization in Federated Learning	Oct 5, 2023	Federated Learning	CodeCode Available
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation	Sep 5, 2023	Domain AdaptationFederated Learning	CodeCode Available

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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