Problem
Research questions and friction points this paper is trying to address.
collaborative learning
graph-structured data
Euclidean data
federated learning
decentralized learning
From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning
🤖 AI Summary
该文探讨了从欧几里得数据到图结构数据的协作学习方法，以应对传统机器学习在可扩展性和隐私性上的局限。
📝 Abstract
The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundamental limitations, including scalability and privacy, that restrict its applicability. To address these challenges, recent research has explored collaborative learning approaches, including federated learning and decentralized learning, where individual agents perform training and inference locally, with limited collaboration. Most collaborative learning research focuses on Euclidean data with regular, grid-like structure (e.g., images, text). However, these approaches fail to capture the relational patterns in many real-world applications, best represented by graphs. Learning on graphs relies on message-passing mechanisms to propagate information between connected nodes, making it conceptually well-suited for collaborative environments where agents must exchange information. Yet, the opportunities and challenges of learning on graph-structured data in collaborative settings remain largely underexplored. This survey provides a comprehensive investigation of collaborative learning from Euclidean to graph-structured data, aiming to consolidate this emerging field. We begin by reviewing its foundational principles for Euclidean data, organizing them along three core dimensions: learning effectiveness, efficiency, and privacy preservation. We then extend the discussion to graph-structured data, introducing a taxonomy of graph distribution scenarios, characterizing associated statistical heterogeneities, and developing standardized problem formulations and algorithmic frameworks. Finally, we systematically identify open challenges and promising research directions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Collaborative Learning
Graph-Structured Data
Federated Learning
Decentralized Learning
Message-Passing Mechanisms
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