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 4526–4550 of 6771 papers

Title	Date	Tasks	Status
Flashback: Understanding and Mitigating Forgetting in Federated Learning	Feb 8, 2024	Federated Learning	—Unverified
FLASH: Federated Learning Across Simultaneous Heterogeneities	Feb 13, 2024	Federated LearningMulti-Armed Bandits	—Unverified
FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG	Aug 6, 2024	ChatbotFederated Learning	—Unverified
FLBoost: On-the-Fly Fine-tuning Boosts Federated Learning via Data-free Distillation	Sep 29, 2021	Federated Learning	—Unverified
FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA	Mar 29, 2023	ClusteringFederated Learning	—Unverified
FLCert: Provably Secure Federated Learning against Poisoning Attacks	Oct 2, 2022	Federated Learning	—Unverified
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences	Oct 18, 2022	Federated LearningStochastic Optimization	—Unverified
FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems	Jun 8, 2023	BenchmarkingEdge-computing	—Unverified
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks	Oct 3, 2023	Federated Learning	—Unverified
FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction	Jun 12, 2020	Federated Learning	—Unverified
Improving Local Training in Federated Learning via Temperature Scaling	Jan 18, 2024	Federated Learning	—Unverified
FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments	May 19, 2025	Activity RecognitionFederated Learning	—Unverified
Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings	Jul 26, 2023	Federated LearningVertical Federated Learning	—Unverified
Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost	Jan 19, 2022	Federated Learning	—Unverified
FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments	Oct 31, 2023	Deep LearningFederated Learning	—Unverified
FL Games: A federated learning framework for distribution shifts	May 23, 2022	Federated Learning	—Unverified
FL Games: A Federated Learning Framework for Distribution Shifts	Oct 31, 2022	Federated Learning	—Unverified
FLGo: A Fully Customizable Federated Learning Platform	Jun 21, 2023	Federated Learning	—Unverified
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning	Mar 7, 2024	Federated Learning	—Unverified
FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning	Jan 1, 2024	BenchmarkingFederated Learning	—Unverified
FLHub: a Federated Learning model sharing service	Feb 14, 2022	BIG-bench Machine LearningDistributed Computing	—Unverified
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN	Mar 25, 2024	Federated LearningPrivacy Preserving	—Unverified
FLINT: A Platform for Federated Learning Integration	Feb 24, 2023	Federated Learning	—Unverified
Privacy-Preserving Federated Learning via Dataset Distillation	Oct 25, 2024	AttributeDataset Distillation	—Unverified
FLIPS: Federated Learning using Intelligent Participant Selection	Aug 7, 2023	ClusteringFederated Learning	—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