Collaborative Machine Learning

Collaborative machine learning focuses on training machine learning models across multiple decentralized devices while preserving data privacy. Current research emphasizes addressing challenges like data heterogeneity through personalized models, robust aggregation techniques (e.g., using randomized aggregation or support vector machines), and secure communication protocols in decentralized or peer-to-peer settings. This field is significant for enabling large-scale model training with sensitive data, fostering trust and fairness among participants, and improving the efficiency and robustness of machine learning systems in various applications.

Papers

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

July 22, 2023

Collaboratively Learning Linear Models with Structured Missing Data
Chen Cheng, Gary Cheng, John Duchi
Agent Smith Least Square Linear Estimator Collaborative Machine Learning Human Algorithm

April 17, 2023

Collaborative Feature Learning for Fine-grained Facial Forgery Detection and Segmentation
Weinan Guan, Wei Wang, Jing Dong, Bo Peng, Tieniu Tan
Fine Grained Segmentation Based Approach Forgery Detection Face Forgery Collaborative Machine Learning Manipulation Detection

April 11, 2023

Collaborative Machine Learning Model Building with Families Using Co-ML
Tiffany Tseng, Jennifer King Chen, Mona Abdelrahman, Mary Beth Kery, Fred Hohman, Adriana Hilliard, R. Benjamin Shapiro
Machine Learning Machine Learning System Single Parent Family Collaborative Machine Learning Model Collaboration

February 27, 2023

Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain
Baturalp Buyukates, Chaoyang He, Shanshan Han, Zhiyong Fang, Yupeng Zhang, Jieyi Long, Ali Farahanchi, Salman Avestimehr
Product Design Model Training Blockchain Based Platform Smart Contract Collaborative Machine Learning Data Market

February 20, 2023

Federated Gradient Matching Pursuit
Halyun Jeong, Deanna Needell, Jing Qin
Sparsity Increase Decentralized Data Collaborative Machine Learning Sparsity Constraint Fast Convergence Sparse Federated

February 15, 2023

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning
Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medić, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci
Decentralized Machine Learning Collaborative Machine Learning Risc V

December 14, 2022

Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
Wireless Network Task Scheduling Asynchronous Federated Learning Device Training Collaborative Machine Learning Aggregation Method Limited Network

December 1, 2022

PiPar: Pipeline Parallelism for Collaborative Machine Learning
Zihan Zhang, Philip Rodgers, Peter Kilpatrick, Ivor Spence, Blesson Varghese
Deep Learning Model Novel Framework Collaborative Machine Learning Pipeline Parallelism

November 7, 2022

Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks
Naoya Tezuka, Hideya Ochiai, Yuwei Sun, Hiroshi Esaki
Federated Learning Tiny Refinement Elicit Resilience Collaborative Machine Learning Model Poisoning Attack Strong Model Wireless Node

October 19, 2022

Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning
Liangkun Yu, Xiang Sun, Rana Albelaihi, Chen Yi
Federated Learning Federated Prompt Cooperation Model Aggregation Collaborative Machine Learning Faster Convergence Speed

September 12, 2022

An Investigation of Smart Contract for Collaborative Machine Learning Model Training
Shengwen Ding, Chenhui Hu
Model Training Blockchain Based Platform Smart Contract Collaborative Machine Learning

May 24, 2022

Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning
Hideya Ochiai, Yuwei Sun, Qingzhe Jin, Nattanon Wongwiwatchai, Hiroshi Esaki
Federated Learning Collaborative Machine Learning Sensor Node Opportunistic Routing

January 4, 2022

Survey on the Convergence of Machine Learning and Blockchain
Shengwen Ding, Chenhui Hu
Machine Learning Timely Survey Machine Learning Model Early Stage Convergence Blockchain Based Platform Collaborative Machine Learning

Collaborative Machine Learning

Papers

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

Collaboratively Learning Linear Models with Structured Missing Data

Collaborative Feature Learning for Fine-grained Facial Forgery Detection and Segmentation

Collaborative Machine Learning Model Building with Families Using Co-ML

Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain

Federated Gradient Matching Pursuit

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning

Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks

PiPar: Pipeline Parallelism for Collaborative Machine Learning

Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks

Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning

An Investigation of Smart Contract for Collaborative Machine Learning Model Training

Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning

Survey on the Convergence of Machine Learning and Blockchain