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 701–725 of 6771 papers

Title	Date	Tasks	Status	Hype
Delay Optimization of a Federated Learning-based UAV-aided IoT network	Feb 10, 2025	Federated Learning	—Unverified	0
Secure Visual Data Processing via Federated Learning	Feb 9, 2025	Federated LearningManagement	—Unverified	0
Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection	Feb 8, 2025	Anomaly DetectionComputational Efficiency	CodeCode Available	0
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning	Feb 8, 2025	Anomaly DetectionFederated Learning	CodeCode Available	0
Principles and Components of Federated Learning Architectures	Feb 7, 2025	Federated Learning	—Unverified	0
Using Federated Machine Learning in Predictive Maintenance of Jet Engines	Feb 7, 2025	Federated Learning	—Unverified	0
Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning	Feb 7, 2025	Federated LearningInductive Bias	—Unverified	0
Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs	Feb 7, 2025	Federated LearningMedical Question Answering	CodeCode Available	1
Graph Federated Learning Based Proactive Content Caching in Edge Computing	Feb 7, 2025	Edge-computingFederated Learning	—Unverified	0
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences	Feb 7, 2025	Federated Learningmodel	—Unverified	0
Federated Learning for Anomaly Detection in Energy Consumption Data: Assessing the Vulnerability to Adversarial Attacks	Feb 7, 2025	Anomaly DetectionFederated Learning	—Unverified	0
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation	Feb 7, 2025	AttributeFederated Learning	—Unverified	0
Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction	Feb 6, 2025	Federated LearningMRI Reconstruction	CodeCode Available	1
Non-convex composite federated learning with heterogeneous data	Feb 6, 2025	Federated Learning	—Unverified	0
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks	Feb 6, 2025	Federated LearningTransfer Learning	—Unverified	0
Comparing privacy notions for protection against reconstruction attacks in machine learning	Feb 6, 2025	Federated Learning	—Unverified	0
SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning	Feb 6, 2025	BenchmarkingData Poisoning	CodeCode Available	2
Private Federated Learning In Real World Application -- A Case Study	Feb 6, 2025	Federated Learning	—Unverified	0
The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective	Feb 5, 2025	Federated Learning	—Unverified	0
FedP^2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs	Feb 5, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Interaction-Aware Gaussian Weighting for Clustered Federated Learning	Feb 5, 2025	ClusteringFederated Learning	—Unverified	0
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing	Feb 5, 2025	Federated LearningScheduling	CodeCode Available	0
Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks	Feb 5, 2025	Federated LearningManagement	—Unverified	0
Multi-objective methods in Federated Learning: A survey and taxonomy	Feb 5, 2025	FairnessFederated Learning	—Unverified	0
A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments	Feb 4, 2025	Cloud ComputingFederated Learning	—Unverified	0

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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