Federated Split Learning

Federated Split Learning (FSL) is a distributed machine learning paradigm aiming to improve the efficiency and privacy of training large models across multiple devices with limited resources. Current research focuses on optimizing model splitting strategies for various architectures, including large language models and vision transformers, often incorporating techniques like momentum alignment and auxiliary networks to reduce communication overhead and enhance model accuracy. This approach is particularly significant for applications like personalized AI agents and resource-constrained IoT environments, offering a balance between data privacy and computational efficiency compared to traditional centralized or fully federated learning methods.

Papers

November 9, 2024

Federated Split Learning for Human Activity Recognition with Differential Privacy
Josue Ndeko, Shaba Shaon, Aubrey Beal, Avimanyu Sahoo, Dinh C. Nguyen
Differential Privacy Activity Recognition Federated Split Learning

July 12, 2024

FedsLLM: Federated Split Learning for Large Language Models over Communication Networks
Kai Zhao, Zhaohui Yang, Chongwen Huang, Xiaoming Chen, Zhaoyang Zhang
Large Language Model Low Rank Adaptation Split Learning Computer Network Federated Split Learning Splitfed Learning

June 12, 2024

A deep cut into Split Federated Self-supervised Learning
Marcin Przewięźlikowski, Marcin Osial, Bartosz Zieliński, Marek Śmieja
Self Supervised Learning Momentum Based Cut and Approximate Split Federated Learning Federated Split Learning Depth Separation Network Layer

May 31, 2024

Non-Federated Multi-Task Split Learning for Heterogeneous Sources
Yilin Zheng, Atilla Eryilmaz
Data Heterogeneity Heterogeneous Data Split Learning Heterogeneous Computing Heterogeneous Source Federated Split Learning

May 22, 2024

Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training
Zhiyuan Wang, Bokui Chen, Xiaoyang Qu, Zhenhou Hong, Jing Xiao, Jianzong Wang
Multi Agent Split Learning Decision Transformer Decision Making Agent Federated Split Learning

March 24, 2024

Heterogeneous Federated Learning with Splited Language Model
Yifan Shi, Yuhui Zhang, Ziyue Huang, Xiaofeng Yang, Li Shen, Wei Chen, Xueqian Wang
Language Model Vision Transformer Split Learning Heterogeneous Federated Learning Gradient Inversion Attack Federated Split Learning

October 23, 2023

FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients
Jiyun Shin, Jinhyun Ahn, Honggu Kang, Joonhyuk Kang
Foundation Model Federated Learning Large Model Heterogeneous Client Federated Split Learning

September 7, 2023

Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision
David Naseh, Swapnil Sadashiv Shinde, Daniele Tarchi
LeArning Abstract BNN Layer Federated Split Learning Space Air Ground Integrated Network Non Terrestrial Network

August 20, 2023

FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection
Naif Alkhunaizi, Koushik Srivatsan, Faris Almalik, Ibrahim Almakky, Karthik Nandakumar
Domain Generalization Unseen Domain Intermediate Representation Face Presentation Attack Detection Federated Split Learning

July 25, 2023

Federated Split Learning with Only Positive Labels for resource-constrained IoT environment
Praveen Joshi, Chandra Thapa, Mohammed Hasanuzzaman, Ted Scully, Haithem Afli
Split Learning Resource Constrained Collaborative Machine Learning Federated Split Learning Splitfed Learning Collaborative Distributed Machine Learning

June 26, 2023

FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
Faris Almalik, Naif Alkhunaizi, Ibrahim Almakky, Karthik Nandakumar
Vision Transformer Split Learning Medical Image Classification Task Federated Split Learning

May 22, 2023

When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework
Xinjing Yuan, Lingjun Pu, Lei Jiao, Xiaofei Wang, Meijuan Yang, Jingdong Xu
Machine Learning Federated Split Learning

February 11, 2023

Communication and Storage Efficient Federated Split Learning
Yujia Mu, Cong Shen
Timely Communication Split Learning Parallel Training Federated Split Learning

January 4, 2023

Privacy and Efficiency of Communications in Federated Split Learning
Zongshun Zhang, Andrea Pinto, Valeria Turina, Flavio Esposito, Ibrahim Matta
High Efficiency Privacy Policy Timely Communication Federated Prompt Cooperation Split Learning Federated Split Learning Privacy Inference

September 2, 2022

Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning
Benshun Yin, Zhiyong Chen, Meixia Tao
Multi Objective Optimization Split Learning Edge Intelligence Splitting Algorithm Collaborative Training Model Splitting Federated Split Learning

Federated Split Learning

Papers

Federated Split Learning for Human Activity Recognition with Differential Privacy

FedsLLM: Federated Split Learning for Large Language Models over Communication Networks

A deep cut into Split Federated Self-supervised Learning

Non-Federated Multi-Task Split Learning for Heterogeneous Sources

Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training

Heterogeneous Federated Learning with Splited Language Model

FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients

Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision

FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection

Federated Split Learning with Only Positive Labels for resource-constrained IoT environment

FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling

When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework

Communication and Storage Efficient Federated Split Learning

Privacy and Efficiency of Communications in Federated Split Learning

Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning