Federated Learning

1217 papers with code • 12 benchmarks • 11 datasets

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.

Libraries

Use these libraries to find Federated Learning models and implementations

FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation

liu-yuxi/fedfms 8 Mar 2024

The Segmentation Anything Model (SAM) serves as a powerful foundation model for visual segmentation and can be adapted for medical image segmentation.

13
08 Mar 2024

FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling

orion-orion/fedhcdr 5 Mar 2024

Specifically, to address the data heterogeneity across domains, we introduce an approach called hypergraph signal decoupling (HSD) to decouple the user features into domain-exclusive and domain-shared features.

7
05 Mar 2024

PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning

colazhang22/pps-qmix 5 Mar 2024

Agents share Q-value network periodically during the training process.

2
05 Mar 2024

FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models

201younghanlee/flguard 5 Mar 2024

However, recent research proposed poisoning attacks that cause a catastrophic loss in the accuracy of the global model when adversaries, posed as benign clients, are present in a group of clients.

1
05 Mar 2024

Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer Networks

ehsannowroozi/federatedlearning_poison_lf_fp 5 Mar 2024

In LF, we randomly flipped the labels of benign data and trained the model on the manipulated data.

0
05 Mar 2024

Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling

elegy112138/fedcmd 4 Mar 2024

To address these issues, we propose a novel federated learning framework called FedCMD, a model decoupling tailored to the Cloud-edge supported federated learning that separates deep neural networks into a body for capturing shared representations in Cloud and a personalized head for migrating data heterogeneity.

2
04 Mar 2024

Analysis of Privacy Leakage in Federated Large Language Models

vunhatminh/fl_attacks 2 Mar 2024

With the rapid adoption of Federated Learning (FL) as the training and tuning protocol for applications utilizing Large Language Models (LLMs), recent research highlights the need for significant modifications to FL to accommodate the large-scale of LLMs.

1
02 Mar 2024

Global and Local Prompts Cooperation via Optimal Transport for Federated Learning

hongxialee/fedotp 29 Feb 2024

Specifically, for each client, we learn a global prompt to extract consensus knowledge among clients, and a local prompt to capture client-specific category characteristics.

12
29 Feb 2024

On the Convergence of Federated Learning Algorithms without Data Similarity

alibeikmohammadi/fedalgo_wo_datasim 29 Feb 2024

In this paper, we present a novel and unified framework for analyzing the convergence of federated learning algorithms without the need for data similarity conditions.

1
29 Feb 2024

Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos

destiny301/uefl 29 Feb 2024

Federated learning (FL), aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos.

0
29 Feb 2024