Federated Learning Client

Federated learning (FL) clients are the individual devices participating in collaborative model training without sharing raw data. Research currently focuses on optimizing client selection strategies to improve efficiency and accuracy, addressing challenges like data heterogeneity and limited bandwidth, often employing techniques like gradient projection, clustering, and contrastive learning. These advancements aim to enhance the performance and practicality of FL, particularly in resource-constrained environments like mobile and IoT networks, while mitigating privacy risks associated with centralized training.

Papers

November 3, 2024

Federated Learning Clients Clustering with Adaptation to Data Drifts
Minghao Li (1), Dmitrii Avdiukhin (2), Rana Shahout (1), Nikita Ivkin (3), Vladimir Braverman (4), Minlan Yu (1) ((1) Harvard University, (2) Northwestern University, (3) Amazon, (4) Rice University)
Adaptation Concern Data Drift Client Heterogeneity Clustered Federated Learning Adaptive Clustering Federated Learning Client

October 25, 2024

Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning
Mengmeng Chen, Xiaohu Wu, Xiaoli Tang, Tiantian He, Yew-Soon Ong, Qiqi Liu, Qicheng Lao, Han Yu
Efficient Network Federated Learning Client

July 17, 2024

CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering
Ensieh Khazaei, Alireza Esmaeilzehi, Bilal Taha, Dimitrios Hatzinakos
Contrastive Learning Activity Recognition Sensor Data Deep Clustering Privacy Preserving Image Federated Image Classification Federated Learning Client

May 24, 2024

Towards Client Driven Federated Learning
Songze Li, Chenqing Zhu
Federated Learning Client Collaboration Client Model Federated Learning Client

March 26, 2024

GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning
Shijie Na, Yuzhi Liang, Siu-Ming Yiu
Federated Learning Natural Gradient Net Present Value Client Selection Cifar 10 Federated Learning Client

January 18, 2024

Vulnerabilities of Foundation Model Integrated Federated Learning Under Adversarial Threats
Chen Wu, Xi Li, Jiaqi Wang
Foundation Model Federated Learning Security Vulnerability Adversarial Threat Federated Learning Client

September 30, 2023

Accelerating Non-IID Federated Learning via Heterogeneity-Guided Client Sampling
Huancheng Chen, Haris Vikalo
Client Selection Statistical Heterogeneity Client Sampling Federated Learning Client Based Sampling

June 14, 2023

A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation
Ronast Subedi, Rebati Raman Gaire, Sharib Ali, Anh Nguyen, Danail Stoyanov, Binod Bhattarai
Domain Adaptation Facial Datasets Federated Learning Client Surgical Video Segmentation

May 11, 2023

Multi-Tier Client Selection for Mobile Federated Learning Networks
Yulan Gao, Yansong Zhao, Han Yu
Lyapunov Function Client Selection Resource Constrained Device Federated Learning Client Harmony Search

February 14, 2023

FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks
Jiajun Wu, Steve Drew, Jiayu Zhou
Performance Improvement Internet of Thing Network Edge Network Federated Learning Client

December 3, 2022

GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Shiqi He, Qifan Yan, Feijie Wu, Lanjun Wang, Mathias Lécuyer, Ivan Beschastnikh
Model Compression Masked Modeling Client Sampling Phonetically Noised Glue Federated Learning Client

November 25, 2022

MDA: Availability-Aware Federated Learning Client Selection
Amin Eslami Abyane, Steve Drew, Hadi Hemmati
Client Selection Federated Learning Client Faster Client

November 5, 2022

ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme
Mario Chahoud, Hani Sami, Azzam Mourad, Safa Otoum, Hadi Otrok, Jamal Bentahar, Mohsen Guizani
Decentralized Data Federated Deep Kubernetes Cluster Federated Learning Client Smartphone Data

August 12, 2022

A Fast Blockchain-based Federated Learning Framework with Compressed Communications
Laizhong Cui, Xiaoxin Su, Yipeng Zhou
Federated Learning Convex Loss Blockchain Network Federated Learning Client Compressed Communication

February 28, 2022

Improving Response Time of Home IoT Services in Federated Learning
Dongjun Hwang, Hyunsu Mun, Youngseok Lee
Federated Learning Internet of Thing Data Federated Learning Client IoT Service

Federated Learning Client

Papers

Federated Learning Clients Clustering with Adaptation to Data Drifts

Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning

CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering

Towards Client Driven Federated Learning

GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning

Vulnerabilities of Foundation Model Integrated Federated Learning Under Adversarial Threats

Accelerating Non-IID Federated Learning via Heterogeneity-Guided Client Sampling

A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation

Multi-Tier Client Selection for Mobile Federated Learning Networks

FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks

GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning

MDA: Availability-Aware Federated Learning Client Selection

ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme

A Fast Blockchain-based Federated Learning Framework with Compressed Communications

Improving Response Time of Home IoT Services in Federated Learning