The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape
Joshua C. Zhao, Saurabh Bagchi, Salman Avestimehr, Kevin S. Chan,, Somali Chaterji, Dimitris Dimitriadis, Jiacheng Li, Ninghui Li, Arash, Nourian, Holger R. Roth

TL;DR
This survey reviews federated learning's privacy challenges, attacks, defenses, applications, and regulatory landscape, emphasizing the need for improved privacy-preserving techniques to enable secure collaborative machine learning.
Contribution
It provides a comprehensive overview of privacy attacks and defenses in federated learning, highlighting current limitations and future research directions.
Findings
Privacy attacks can often infer sensitive data from model updates
Existing defenses have limitations in fully protecting client privacy
Successful industry applications demonstrate federated learning's potential
Abstract
Deep learning has shown incredible potential across a wide array of tasks, and accompanied by this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices, and recent concerns on privacy have further highlighted challenges for accessing such data. As a result, federated learning (FL) has emerged as an important privacy-preserving technology that enables collaborative training of machine learning models without the need to send the raw, potentially sensitive, data to a central server. However, the fundamental premise that sending model updates to a server is privacy-preserving only holds if the updates cannot be "reverse engineered" to infer information about the private training data. It has been shown under a wide variety of settings that this privacy premise does not hold. In this survey paper,…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security
