2023/07/22 by Cheng Chen, Cheng, Chen, Gary Cheng +3 · 1 citation
Computer Science · #Data Stream Mining Techniques #Distributed #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mobile Crowdsensing and Crowdsourcing #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2307.11947
openalex publication_date 2023/07/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the problem of collaboratively learning least squares estimates for m agents. Each agent observes a different subset of the features\unicodex2013e.g., containing data collected from sensors of varying resolution. Our goal is to determine how to coordinate the agents in order to produce the best estimator for each agent. We propose a distributed, semi-supervised algorithm Collab, consisting of three steps: local training, aggregation, and distribution. Our procedure does not require communicating the labeled data, making it communication efficient and useful in settings where the labeled data is inaccessible. Despite this handicap, our procedure is nearly asymptotically local minimax optimal\unicodex2013even among estimators allowed to communicate the labeled data such as imputation methods. We test our method on real and synthetic data.