Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning
This work provides a theoretical foundation for understanding trade-offs in learning settings, which is incremental as it extends existing information-theoretic analysis to federated learning.
The authors tackled the problem of analyzing generalization error and privacy leakage across classical, distributed, and federated learning paradigms by developing an information-theoretic framework, resulting in derived upper and lower bounds for these metrics.
Machine learning algorithms operating on mobile networks can be characterized into three different categories. First is the classical situation in which the end-user devices send their data to a central server where this data is used to train a model. Second is the distributed setting in which each device trains its own model and send its model parameters to a central server where these model parameters are aggregated to create one final model. Third is the federated learning setting in which, at any given time $t$, a certain number of active end users train with their own local data along with feedback provided by the central server and then send their newly estimated model parameters to the central server. The server, then, aggregates these new parameters, updates its own model, and feeds the updated parameters back to all the end users, continuing this process until it converges. The main objective of this work is to provide an information-theoretic framework for all of the aforementioned learning paradigms. Moreover, using the provided framework, we develop upper and lower bounds on the generalization error together with bounds on the privacy leakage in the classical, distributed and federated learning settings. Keywords: Federated Learning, Distributed Learning, Machine Learning, Model Aggregation.