Client Collaboration

Client collaboration in machine learning focuses on training shared models across multiple clients while preserving data privacy and addressing data heterogeneity. Current research emphasizes developing algorithms and model architectures (like personalized federated learning and various adaptations of large language models) that efficiently handle non-independent and identically distributed (non-IID) data, optimize communication costs, and account for varying client resources and capabilities. This field is significant for enabling large-scale machine learning applications while respecting data privacy regulations and improving model performance in diverse settings, with applications ranging from finance to healthcare.

Papers

July 29, 2023

UPFL: Unsupervised Personalized Federated Learning towards New Clients
Tiandi Ye, Cen Chen, Yinggui Wang, Xiang Li, Ming Gao
Personalized Federated Learning Personalized Model Client Collaboration Risk Minimization Knowledge Distillation Loss

May 30, 2023

Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting
Jiaxin Gao, Wenbo Hu, Yuntian Chen
Transformer Based Model Multivariate Time Series Forecasting Long Term Time Series Forecasting Client Collaboration

April 21, 2023

Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning
Jieming Bian, Cong Shen, Jie Xu
Global Model Client Collaboration Communication Efficient Federated Learning Model Exchange

December 2, 2022

PGFed: Personalize Each Client's Global Objective for Federated Learning
Jun Luo, Matias Mendieta, Chen Chen, Shandong Wu
Personalized Federated Learning Implicit Knowledge Client Collaboration Group Consensus Adaptive Risk

November 25, 2022

Strategic Facility Location with Clients that Minimize Total Waiting Time
Simon Krogmann, Pascal Lenzner, Alexander Skopalik
Client Collaboration Client Heterogeneity Congestion Game Facility Location

November 7, 2022

Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning
Hongrui Shi, Valentin Radu, Po Yang
Heterogeneous Federated Learning Global Model Client Collaboration Client Model Heterogeneity Challenge

October 6, 2022

DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Jiawei Shao, Yuchang Sun, Songze Li, Jun Zhang
Federated Learning Private Data Private Datasets Client Collaboration Local Gradient

July 12, 2022

Federated Unlearning: How to Efficiently Erase a Client in FL?
Anisa Halimi, Swanand Kadhe, Ambrish Rawat, Nathalie Baracaldo
Unlearning Framework Harmful Unlearning Unlearning Method Unlearned Model Federated Unlearning Client Collaboration

July 10, 2022

FedSS: Federated Learning with Smart Selection of clients
Ammar Tahir, Yongzhou Chen, Prashanti Nilayam
Client Selection Selection Policy Client Collaboration Faster Client

June 17, 2022

Decentralized adaptive clustering of deep nets is beneficial for client collaboration
Edvin Listo Zec, Ebba Ekblom, Martin Willbo, Olof Mogren, Sarunas Girdzijauskas
Deep Network Decentralized FL Gossip Algorithm Client Collaboration Local Learning Peer to Peer Soft Clustering Adaptive Clustering Client Clustering

June 11, 2022

Federated Learning with GAN-based Data Synthesis for Non-IID Clients
Zijian Li, Jiawei Shao, Yuyi Mao, Jessie Hui Wang, Jun Zhang
Generative Adversarial Network Synthetic Data Synthetic Dataset Client Collaboration

June 5, 2022

Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling
Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik
Stochastic Gradient Descent Flexible Framework Fast Rate Data Sampling Client Collaboration Stochastic First Order Method Nonconvex Function Smooth Nonconvex

April 29, 2022

Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data
Shadha Tabatabai, Ihab Mohammed, Basheer Qolomany, Abdullatif Albasser, Kashif Ahmad, Mohamed Abdallah, Ala Al-Fuqaha
Machine Learning Environment Exploration Real World eXploitation Client Collaboration Evolutionary Method Poisoned Data

November 4, 2021

A Personalized Federated Learning Algorithm: an Application in Anomaly Detection
Ali Anaissi, Basem Suleiman
Anomaly Detection Application Proficiency Personalized Federated Learning FedAvg Algorithm Centralized Machine Learning Client Collaboration Optimal Communication

Client Collaboration

Papers

UPFL: Unsupervised Personalized Federated Learning towards New Clients

Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting

Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning

PGFed: Personalize Each Client's Global Objective for Federated Learning

Strategic Facility Location with Clients that Minimize Total Waiting Time

Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning

DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing

Federated Unlearning: How to Efficiently Erase a Client in FL?

FedSS: Federated Learning with Smart Selection of clients

Decentralized adaptive clustering of deep nets is beneficial for client collaboration

Federated Learning with GAN-based Data Synthesis for Non-IID Clients

Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling

Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data

A Personalized Federated Learning Algorithm: an Application in Anomaly Detection