Aggregated Model Spaces

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In many practical applications, machine learning is divided over multiple agents, where each agent learns a different task and/or learns from a different dataset. We present Aggregated Model Spaces (AMS), a framework for learning a global model by aggregating local learnings performed by each agent. Our approach forgoes sharing of data between agents, makes no assumptions on the distribution of data across agents, and requires minimal communication between agents. We empirically validate our techniques on MNIST experiments and discuss how AMS can generalize to a wide range of problem settings, including federated averaging and catastrophic forgetting. We believe our framework to be among the first to lay out a general methodology for “combining” distinct models.


Type of resource text
Date created [ca. May 8, 2018]


Author Guha, Neel
Degree granting institution Stanford University, Department of Computer Science
Primary advisor Sahami, Mehran


Subject Machine Learning
Subject Privacy
Subject Knowledge Aggregation
Subject Computer Science
Genre Thesis

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Guha, Neel. (2018). Aggregated Model Spaces. Stanford Digital Repository. Available at:


Undergraduate Theses, School of Engineering

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