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arXiv · 2110.05014

An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints

Sharu Theresa Jose·Osvaldo Simeone
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Abstract

In vertical federated learning (FL), the features of a data sample are distributed across multiple agents. As such, inter-agent collaboration can be beneficial not only during the learning phase, as is the case for standard horizontal FL, but also during the inference phase. A fundamental theoretical question in this setting is how to quantify the cost, or performance loss, of decentralization for learning and/or inference. In this paper, we consider general supervised learning problems with any number of agents, and provide a novel information-theoretic quantification of the cost of decentralization in the presence of privacy constraints on inter-agent communication within a Bayesian framework. The cost of decentralization for learning and/or inference is shown to be quantified in terms of conditional mutual information terms involving features and label variables.

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Sharu Theresa Jose, Osvaldo Simeone. 2021-10-11. An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints. https://doi.org/10.3390/e24040485

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