Advances and Open Problems in Federated Learning

TL;DR

This paper reviews recent progress in federated learning, highlighting its privacy benefits, and discusses open challenges to guide future research in decentralized collaborative machine learning.

Contribution

It provides a comprehensive overview of advances in federated learning and identifies key open problems and challenges for future investigation.

Findings

Federated learning enhances privacy by decentralizing data training.

Recent research has made significant progress in FL algorithms and applications.

Open problems include scalability, communication efficiency, and security concerns.

Abstract

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.