---
abstract: 'Federated learning is gaining significant interests as it enables model training over a large volume of data that is distributedly stored over many users, while protecting the privacy of the individual users. However, a major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users. In fact, the overhead of state-of-the-art protocols for secure model aggregation grows quadratically with the number of users. We propose a new scheme, named [[Turbo-Aggregate]{}]{}, that in a network with $N$ users achieves a secure aggregation overhead of $O(N\log{N})$, as opposed to $O(N^2)$, while tolerating up to a user dropout rate of $50\%$. [[Turbo-Aggregate]{}]{} employs a multi-group circular strategy for efficient model aggregation, and leverages additive secret sharing and novel coding techniques for injecting aggregation redundancy in order to handle user dropouts while guaranteeing user privacy. We experimentally demonstrate that [[Turbo-Aggregate]{}]{} achieves a total running time that grows almost linear in the number of users, and provides up to $14\times$ speedup over the state-of-the-art schemes with upto $N=200$ users. We also experimentally evaluate the impact of several key network parameters (e.g., user dropout rate, bandwidth, and model size) on the performance of [[Turbo-Aggregate]{}]{}.'
author:
- |
    Jinhyun So, Basak Guler, and A. Salman Avestimehr\
    Department of Electrical and Computer Engineering\
    University of Southern California, Los Angeles, CA, USA\
bibliography:
- 'main.bib'
title: '[[Turbo-Aggregate]{}]{}: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning'
---

Introduction
============

 \[1-Intro\]

System Model
============

 \[2-ProbSetting\]

The [[Turbo-Aggregate]{}]{} Protocol
====================================

 \[3-Algorithm\]

Theoretical Guarantees of [[Turbo-Aggregate]{}]{}
=================================================

 \[5-MainResults\]

Experiments
===========

 \[6-Experimental Evaluation\]

Conclusion
==========

 \[7-Conclusion\]

Acknowledgments {#acknowledgments .unnumbered}
===============

This material is based upon work supported by Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001117C0053, ARO award W911NF1810400, NSF grants CCF-1703575, ONR Award No. N00014-16-1-2189, and CCF-1763673. The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.
